Keywords:

semantic differential test . . . . . . .

Résumé

La pollution sonore est un problème majeur pour les résidents des zones urbaines. La directive Européenne 2002/49/CE impose aux états membres l'établissement de cartes de bruit. Ces dernières sont construites sur la base de l'indice énergétique L den , également utilisé dans les relations dose-effet établies pour prédire la gêne. Toutefois, pour l'évaluation de la gêne due au bruit dans les zones urbaines, la pertinence de cet indice est souvent remise en question. En effet, de nombreuses études ont montré que les caractéristiques temporelles et spectrales des bruits environnementaux influencent aussi les réponses de gêne et ne sont pas prises en compte dans cet indice. Cette thèse vise à contribuer à l'amélioration de la caractérisation de la gêne due au bruit des véhicules routiers en ville. Elle est basée sur des expériences réalisées en laboratoire. Elle comprend deux parties principales. La première partie a comme objectif de progresser sur la caractérisation physique et perceptive du bruit des passages de divers véhicules routiers en milieu urbain tels que les bus, les deux-roues motorisés, les poids lourds et les véhicules légers. Une attention particulière a été portée sur la caractérisation de la gêne due aux deuxroues motorisés qui sont cités parmi les véhicules routiers les plus gênants et sont peu étudiés dans la littérature. Dans cette perspective, un indicateur acoustique caractéristique de la gêne due au bruit routier urbain a été déterminé : il rend compte de différents attributs auditifs gênants en associant la sonie, un indice spectral et deux indices de modulation proposés dans le cadre de ces travaux. Dans les zones urbaines, les riverains sont souvent exposés à la circulation routière en présence d'autres sources de bruit de l'environnement. L'objectif principal de la deuxième partie est la prédiction de la gêne totale due au bruit du trafic routier urbain combiné avec le bruit de tramway. Dans le but de caractériser la gêne totale, les phénomènes perceptifs liés à la combinaison de ces bruits sont tout d'abord étudiés. Ensuite, l'indicateur proposé précédemment pour caractériser la gêne due au bruit des passages de différents véhicules routiers est testé lorsque différents trafics routiers urbains sont considérés. Sur la base de ces résultats, des modèles permettant de caractériser la gêne due au bruit de trafic routier urbain combiné au bruit de tramway ont été proposés.

Mots clés

la gêne de court terme, caractérisation physique et perceptive, bruit de passage de véhicules routiers en milieu urbain, deux-roues motorisés, bruit de trafic routier urbain, indice spectral, indice de modulation, bruit de tramway, mono-exposition, multi-exposition au bruit, gêne totale, expérimentation en laboratoire, environnement simulé.

Synthèse

Aujourd'hui, les résidents des zones urbaines sont exposés à une large variété de bruits de l'environnement qui peuvent être entendus seuls ou combinés. Ce sont des bruits liés aux transports (routier, ferroviaire et aérien), aux travaux (e.g. construction de bâtiments), à l'industrie, à des sources sociales (e.g. les voisins, radio et télévision) ou à des sources de loisirs (e.g. les lecteurs de musique portables, feux d'artifice) [124]. En général, en raison de l'urbanisation continue, de plus en plus de personnes sont impactées par le bruit. Pour les autorités locales, les administrateurs, ainsi que pour le public, la pollution sonore est un problème important. Selon une enquête européenne récente, plus de 44% des répondants estiment que le bruit affecte fortement la santé humaine, ce qui correspond à une augmentation de 3% depuis 2006 [START_REF][END_REF]. La santé est définie par l'OMS comme un état complet de bien-être physique, mental et social [124]. L'exposition au bruit peut entraîner des maladies cardiovasculaires, des troubles cognitifs, des troubles du sommeil, des acouphènes et de la gêne [START_REF] Passchier-Vermeer | Noise and health[END_REF]124]. L'un des principaux effets de l'exposition au bruit est la gêne due au bruit. Une enquête nationale réalisée en 1989 a montré que 35 à 40% de la population française se dit gêné par des bruits dans son environnement domestique. Les bruits de transport sont estimés contribuer largement (55%), et plus particulièrement les deux-roues motorisés (19%), les véhicules légers (17%) et les poids lourds (11%) [START_REF] Dufour | Perception du cadre de vie et attitudes sur l'environnement[END_REF]. Dans une enquête française plus récente, 51% des répondants déclarent être gênés par le bruit, 66% s'estiment gênés par le bruit du trafic routier et 45% par le bruit des deux-roues [START_REF] Roy | La sensibilité des français à leur environnement de proximité[END_REF]. Par ailleurs, il a été montré que la présence de transports publics dans la journée et dans la nuit était un indicateur important de la gêne due au bruit en zone urbaine [START_REF] Paunović | Noise annoyance is related to the presence of urban public transport[END_REF].

Le Parlement Européen et le Conseil de l'Union Européenne ont adopté en 2002 la directive 2002/49/CE (END) relative à l'évaluation et à la gestion du bruit dans l'environnement. L'un des principaux objectifs de l'END est d'établir une base commune pour évaluer l'exposition au bruit dans l'Union Européenne (UE). Les cartographies du bruit doivent être réalisées pour les principales sources de bruit de transport (routes principales, voies ferrées, aéroports) et pour les villes européennes de plus de 100.000 habitants. Les cartes de bruit représentent l'exposition au bruit dans certaines zones d'intérêt. Elles sont basées sur des mesures et des modèles prédictifs de bruit et représentent le bruit au moyen des indices L den (le niveau de pression sonore équivalent jour-soir-nuit) et L night (le niveau de pression sonore équivalent nuit). Pour relier la gêne due à des sources de bruit de transport à l'indice de L den , les relations dose-effet ont été créées [START_REF] Schultz | Synthesis of social surveys on noise annoyance[END_REF][START_REF] Miedema | Exposure-response relationships for transportation noise[END_REF]. Les cartes de bruit peuvent donc être interprétées comme des cartes représentant le niveau de gêne ressentie xiv Synthèse par des résidents. Par ailleurs, différentes études ont bien montré que seul un tiers environ des réponses de gêne peut être expliqué par l'indice énergétique L den , jetant un doute sur sa validité (e.g. [START_REF] Berglund | Community noise in a public health perspective[END_REF][START_REF] Job | Community response to noise: a review of factors influencing the relationship between noise exposure and reaction[END_REF]). Les caractéristiques sonores des bruits de transport et notamment leurs caractéristiques temporelles et spectrales sont connues pour influencer les réponses de gêne (e.g. [START_REF] Ishiyama | The impact of sound quality on annoyance caused by road traffic noise: an influence of frequency spectra on annoyance[END_REF][START_REF] Morel | Physical and perceptual characterization of road traffic noises in urban areas for a better noise annoyance assessment[END_REF][START_REF] Paviotti | On the outdoor annoyance from scooter and motorbike noise in the urban environment[END_REF]). Ceci est vrai en particulier pour les émissions sonores des deux-roues (e.g. motos, motocycles) qui sont aujourd'hui une préoccupation majeure dans les zones urbaines. Au cours de la dernière décennie, leur utilisation a considérablement augmenté [START_REF] Lercher | Exposure by motorbike noise in alpine residential areas -a case study in public health risk assessment[END_REF]. Parmi les véhicules de la route, les deux-roues suscitent des réactions de gêne plus élevées que les autres (e.g. [START_REF] Berglund | Total annoyance and perceptually discernible noise sources[END_REF][START_REF] Paviotti | On the outdoor annoyance from scooter and motorbike noise in the urban environment[END_REF]). Mais dans la littérature, ces véhicules urbains ont été très peu étudiés du point de vue de la gêne ressentie. Des travaux de recherche mentionnent que la gêne due à ces véhicules est due à des caractéristiques spectrales [START_REF] Morel | Physical and perceptual characterization of road traffic noises in urban areas for a better noise annoyance assessment[END_REF] et temporelles relatives à des modulations d'amplitude [START_REF] Paviotti | On the outdoor annoyance from scooter and motorbike noise in the urban environment[END_REF][START_REF] Morel | Physical and perceptual characterization of road traffic noises in urban areas for a better noise annoyance assessment[END_REF].

Du point de vue réglementaire, la cartographie du bruit appliquée par les membres de l'UE porte sur deux catégories de véhicules qui sont les véhicules légers et les poids lourds. Dans une tentative de faire la distinction entre les différents types de véhicules dans la cartographie, une approche commune a été très récemment proposée. Il s'agit de regrouper les véhicules routiers selon quatre catégories (véhicules légers, véhicules lourds moyens, véhicules lourds, motocycles et motos) et éventuellement une autre catégorie pour considérer des véhicules nouvellement développés tels que les voitures hybrides [START_REF] Kephalopoulos | Advances in the development of common noise assessment methods in europe: The cnossos-eu framework for strategic environmental noise mapping[END_REF]. L'inclusion des deux-roues motorisés dans la cartographie stratégique du bruit est certes un progrès, cependant, l'indice L den étant seulement basé sur l'énergie acoustique, il n'est pas capable de rendre compte des caractéristiques spectrales et temporelles influant les réponses de gêne due au bruit des deux-roues. Ainsi, afin d'améliorer la caractérisation de la gêne due au bruit du trafic routier urbain, une caractérisation perceptive des différentes spécificités acoustiques influentes est essentielle. Une attention particulière doit nécessairement être portée sur la caractérisation de la gêne due aux deux-roues motorisés. Selon l'END, les cartes de bruit sont réalisées séparément pour chaque source de bruit. Dans les zones urbaines, cependant, le bruit de la circulation routière n'est généralement pas entendu séparément, mais en combinaison avec d'autres bruits créant de nombreuses situations de multi-exposition. Ces sources de bruit peuvent interagir les unes avec les autres modifiant ainsi les réponses de gêne due à chaque bruit de la combinaison et rendant difficile la prédiction de la gêne totale due aux sources de bruit combinées (e.g [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF][START_REF] Lercher | The effects of noise from combined traffic sources on annoyance: the case of interactions between rail and road noise[END_REF]). La compréhension des situations de multi-exposition est donc essentielle aux instances gouvernementales et européennes pour appliquer des stratégies adéquates de réduction du bruit visant à améliorer l'environnement sonore.

Cette thèse vise à contribuer à l'amélioration de la caractérisation de la gêne due au bruit des véhicules routiers en ville. La première partie a comme objectif d'identifier et mettre en évidence des caractéristiques temporelles et spectrales influençant les réponses de gêne. Le but est de proposer un indicateur acoustique caractéristique de la gêne due au bruit routier urbain qui rende compte de différents attributs auditifs gênants. L'objectif principal de la deuxième partie est de tester l'indicateur proposé lorsque le bruit de trafic routier urbain est entendu en présence d'une autre source de bruit de l'environnement urbain. Le bruit de tramway est considéré à cet effet et les travaux de thèse présentés dans ce mémoire bénéficieront de la caractérisation de la gêne due au bruit de tramway menée récemment [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF][START_REF] Trollé | Corrigendum to short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF]. Dans le but de caractériser la gêne totale due au trafic routier urbain combiné au bruit du tramway, les phénomènes perceptifs liés à la combinaison de ces bruits sont tout d'abord étudiés. Puis est testé l'indicateur de gêne due au bruit routier urbain lorsqu'il est entendu en présence du bruit de tramway, et enfin des modèles pour calculer la gêne totale en situation de multi-exposition sont proposés.

Le premier chapitre de ce mémoire fait état des travaux de la littérature traitant la problématique fixée. Nous nous intéressons notamment à la caractérisation de la gêne due au bruit routier urbain seul, puis, en situation de multi-exposition en présence du bruit du tramway.

Dans le deuxième chapitre, il est proposé pour traiter le premier objectif de ces travaux de thèse de tester différents indices spectraux et temporels présents dans la littérature en les confrontant aux réponses de gêne due à des bruits de passage de véhicules routiers en milieu urbain et recueillies par Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] en conditions contrôlées. Les passages de véhicules routiers étudiés dans mes travaux sont donc ceux enregistrés in situ par Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] et Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF][START_REF] Morel | Physical and perceptual characterization of road traffic noises in urban areas for a better noise annoyance assessment[END_REF]. L'objectif ici est de contribuer à l'amélioration de la caractérisation de la gêne due au bruit selon différentes catégories de la typologie perceptive et cognitive proposée par Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF]. Les indices proposés dans la littérature récente et portant sur la caractérisation de la gêne due aux bruits des transports (e.g. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF]) seront testés. Les résultats relatifs aux indices spectraux montrent que l'indice TETC (énergie totale des composantes tonales dans les bandes critiques) est bien corrélé avec les réponses de gêne par catégorie perceptive. Par contre, par rapport aux indices acoustiques L LF , L MF , L HF appliqués par Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF], une amélioration en termes de coefficients de corrélation n'a pas été observée. En ce qui concerne la caractérisation des spécificités temporelles liées aux variations d'amplitude irrégulières, quatre indices basés sur le niveau de pression sonore pondéré A ont été testés. Une analyse des corrélations entre les réponses moyennes de gêne par catégorie perceptive [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] et les valeurs des indices a montré de fortes corrélations positives avec l'indice STDP (écart-type de la pression pondérée A en fonction de sa variation dans le temps). En comparaison avec les indices proposés par Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] pour caractériser les variations d'amplitude irrégulières (∆L + , ∆L -, ∆N + et ∆N -), des corrélations plus fortes ont été obtenues pour chaque catégorie perceptive. Cependant pour les deux-roues en accélération (Catégorie 2) et en décélération (Catégorie 4) des coefficients de corrélation modérés ont été obtenus.

Deux indices différents de rugosité ont ensuite été testés, ils sont respectivement issus du modèle de rugosité Oldenburg [START_REF] Oetjen | Rauigkeitsberechnung unter Berücksichtigung der Einhüllendenform[END_REF] et du modèle dBSonic (basé sur Aures, cf. [START_REF] Aures | Ein Berechnungsverfahren der Rauhigkeit (A procedure for calculating auditory roughness)[END_REF]). Les résultats montrent que les indices calculés à partir de ces deux modèles sont bien corrélés avec les réponses moyennes de gêne par catégorie. Toutefois, les coefficients de corrélation par catégorie perceptive obtenus à l'aide du modèle de rugosité dBSonic sont généralement supérieurs à ceux obtenus avec xvi Synthèse le modèle Oldenburg. Cependant, les réponses de gêne due au bruit des deux-roues en accélération (Catégorie 2) et au bruit deux-roues en décélération (Catégorie 4) ne sont que modérément corrélées avec les indices de rugosité obtenus avec le modèle dBSonic. Dans une étude pilote réalisée dans le cadre de ces travaux de thèse au laboratoire d'acoustique de l'université d'Oldenburg, les participants avaient été invités à évaluer le caractère "rugueux" de 33 bruits de passage de véhicules routiers provenant de la typologie proposée par Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF]. L'analyse des corrélations a montré que la rugosité mesurée était mieux corrélée avec les valeurs de l'indice R mean obtenus en utilisant le modèle de rugosité dBSonic. Une analyse de modulation du bruit des deux-roues en accélération a mis en évidence la présence de différentes sensations de modulation. Le modèle de rugosité d'Oldenburg prend en compte la forme de l'enveloppe des signaux dans le calcul de la rugosité et de ce fait apparaît plus approprié pour caractériser ces deux-roues. Mais les résultats ont montré que le modèle doit être amélioré, et c'est aussi la conclusion obtenue par Oetjen et al. [START_REF] Oetjen | Rauigkeitsberechnung unter Berücksichtigung der Einhüllendenform[END_REF].

Suite à ces différentes confrontations d'indices, il est apparu nécessaire d'étudier plus en détail les attributs auditifs influençant les réponses de gêne afin d'améliorer la caractérisation de la gêne due aux bruits de passage de véhicules routiers en milieu urbain, et plus particulièrement la caractérisation de la gêne due au bruit des deux-roues.

Le chapitre 3 présente une expérimentation utilisant 14 bruits de passage de véhicules routiers urbains de la typologie de Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF] égalisés au même niveau de pression sonore équivalent pondéré A (L Aeq ). Un test d'écoute basé sur la technique du différentiel sémantique a été construit. Une échelle sémantique portant sur la gêne et couplée à une tâche de verbalisation amenant les participants à expliquer leur réponse de gêne a été proposée. Les adjectifs utilisés pour les échelles sémantiques sont en majorité issus de verbalisations libres recueillies par Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF] pour ces mêmes bruits routiers urbains. D'autres adjectifs ont été rajoutés, ils sont issus de différentiels sémantiques de la littérature appliqués à l'environnement sonore urbain et au bruit des voitures. Une analyse en composantes principales a permis d'identifier quatre dimensions de l'espace perceptif. La première composante est liée au jugement hédonique. La deuxième et la troisième composante concernent respectivement les caractéristiques temporelles et spectrales. La quatrième composante est associée au mouvement de la source et au style de conduite. Au terme de cette analyse, l'importance des caractéristiques temporelles et spectrales pour la perception des bruits de passage de véhicules routiers a bien été démontrée. Grâce à la tâche de verbalisation, les adjectifs associés aux sensations gênantes, ont pu être identifiés. Les principaux adjectifs cités sont relatifs aux caractères "sourd/strident", "pétaradant" et "nasal" des bruits. Les valeurs médianes des réponses recueillies sur les échelles sémantiques correspondantes sont corrélées positivement avec les valeurs médianes pour l'échelle "gênant". L'indice TETC calculé entre les bandes critiques 16 à 24 Barks s'est révélé très approprié pour la caractérisation de la sensation "sourd/strident" et très corrélé à la valeur médiane des réponses sur l'échelle sémantique "gênant".

xvii Un parallèle a pu être établi entre ces résultats et ceux de Trollé et al. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF][START_REF] Trollé | Corrigendum to short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF] portant sur la caractérisation du contenu en hautes fréquences du passage de tramway. Ce résultat atteste qu'un même indice spectral peut être utilisé pour rendre compte de sensations gênantes dues à différents bruits de transport urbain. Les adjectifs "pétaradant" et "nasal" sont associés à des sensations de modulation dues au moteur thermique des véhicules, avec "pétaradant" relatif à des basses fréquences de modulation et "nasal" à des plus hautes fréquences de modulation. Pour caractériser ces sensations, deux indices m sputt,10 et m nas,10 ont été proposés. En effet, ils permettent d'améliorer la caractérisation de ces sensations de modulation par rapport à une caractérisation menée avec les indices classiques de force de fluctuation et de rugosité. En outre, des corrélations positives entre les indices proposés et les valeurs médianes de l'échelle "gênant" ont été trouvées. Nous avons ainsi montré qu'il était possible d'identifier des caractéristiques des bruits de passage de véhicules routiers urbains influençant la gêne. La prochaine étape va être de tester les indices proposés, et cela pour une plus grande variété de bruits de passage des véhicules routiers qui peuvent présenter des différences de niveau sonore.

Dans le chapitre 4, les indices de modulation précédemment proposés ont donc été testés en considérant des bruits de passage de véhicules routiers en milieu urbain sans et avec des niveaux sonores différents entre les véhicules. Deux expérimentations d'évaluation de la gêne due à ces bruits ont été menées. Elles portaient sur un nombre plus élevé de bruits de passage de véhicules routiers correspondant à différents types de véhicules (bus, poids lourd, véhicule léger, 2-roues motorisés) et des allures différentes (fluide, accélérée, décélérée). Dans l'expérience 1, 33 bruits de passage de véhicules routiers de la typologie de Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF] et égalisés au même niveau équivalent de pression sonore pondéré A ont été utilisés. Dans l'expérience 2, les mêmes bruits de passage de véhicules routiers ont été repris. Cependant une dynamique de niveaux entre les différents bruits de passage de véhicules a été introduite. Ces différences de niveau ont été déterminées à partir d'enregistrements in situ menés par Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF]. L'objectif ici était dans un premier temps de tester les indices m sputt,10 et m nas,10 pour la caractérisation de la gêne pour un plus grand nombre de véhicules (expérience 1), puis, d'évaluer leur pertinence en combinaison avec d'autres indices lorsque les différences de niveau sonore entre les véhicules sont considérées (expérience 2). Nous avons ainsi pu observer que l'indice spectral TETC, mis en évidence pour la caractérisation du contenu en hautes fréquences, est fortement corrélé avec les réponses moyennes de gêne. Les indices temporelles m sputt,10 et m nas,10 sont corrélés de manière significative avec les réponses de gêne. Ils rendent compte de sensations dues à des modulations d'amplitude qui ne sont pas prises en compte par les indices de rugosité et de force de fluctuation. Afin d'évaluer la combinaison des indices proposés et de comparer les performances des indices "pétaradant" et "nasal" par rapport aux indices psychoacoustiques classiques, une analyse de régression linéaire multiple a été effectuée. Les conclusions montrent que de meilleurs résultats en termes de R 2 ajusté pour un modèle de régression comprenant les indices "pétaradant", "nasal" et TETC, sont obtenus par rapport à la combinaison xviii Synthèse de l'indice TETC et des indices de rugosité et de force de fluctuation. L'avantage des indices "pétaradant" et "nasal" proposés par rapport aux indices psychoacoustiques est qu'ils sont construits à partir de caractéristiques différentes du signal pour rendre compte de deux sensations différentes de modulation d'amplitude. Ils sont alors statistiquement indépendants et peuvent être utilisés dans un même modèle de régression. Comme nous l'avons vu au chapitre 3, les indices F mean et R 10 ne sont pas toujours pertinents pour rendre compte des sensations de modulation spécifiques aux bruits routiers urbains. De plus, comme le calcul de ces indices dépend de la sonie, ces deux indices sont alors significativement corrélés, et ne peuvent donc être utilisés dans un même modèle de régression pour rendre compte des deux sensations de modulation correspondantes [START_REF] Fastl | Psychoacoustics -Facts and Models[END_REF]. Ce constat souligne le potentiel des deux indices proposés à être combinés pour rendre compte de sensations de modulation différentes qui influencent les réponses de gêne. Dans l'expérience 2, les différences de niveau de pression sonore ont été appliquées aux bruits de passage de véhicules routiers selon le type de véhicule et le type d'allure. La sensation d'intensité était caractérisée par la sonie moyenne (N mean ). Sur la base de corrélations partielles, en contrôlant N mean , l'importance des indices m sputt,10 et m nas,10 et TETC a été démontrée. La combinaison des indices proposés a alors été testée par une analyse de régression linéaire multiple. Les résultats ont montré qu'un indicateur comprenant les indices liés à la modulation m sputt,10 , m nas,10 , la sonie moyenne N mean , et l'indice TETC donne une meilleure qualité d'ajustement du modèle par rapport à un indicateur comprenant N mean , F 10 et TETC.

Il devient maintenant intéressant de tester l'indicateur de gêne proposé prenant en compte les indices de modulation m sputt,10 et m nas,10 , combinés à la sonie moyenne et à l'indice TETC, et ce pour des situations d'exposition au bruit routier urbain plus complexes. On s'est donc intéressé non plus aux bruits de passage de véhicules routiers mais à des bruits de trafic routier urbain. Cette confrontation de l'indicateur de gêne est menée lorsque le bruit de trafic routier urbain est entendu en présence d'une autre source de bruit comme cela est généralement le cas en milieu urbain. En effet dans les zones urbaines, les riverains sont souvent exposés à la circulation routière en présence d'autres bruits. Les bruits peuvent alors interagir entre eux et influencer la gêne due à chacune des sources ainsi que la gêne totale due à ces situations de multi-exposition. Le choix de l'exposition combinée au bruit du trafic routier en zone urbaine et au bruit du tramway a été considéré afin de pouvoir bénéficier de récents travaux traitant de la gêne due au bruit du tramway [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF][START_REF] Trollé | Corrigendum to short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF].

Le Chapitre 5 rend compte de l'étude des phénomènes perceptifs associés à la gêne due à cette exposition combinée. Trois expérimentations portant sur l'évaluation de la gêne ont été réalisées en laboratoire. L'expérience A a été menée en situation imaginaire avec réalisation d'une activité de lecture. Les stimuli de courte durée (3 minutes) étaient diffusés par un système 2.1 dans une salle de test d'écoute. Les expériences B et C ont été réalisées en environnement simulé dans une pièce meublée comme un salon, et avec réalisation d'une activité de lecture. Les stimuli étaient de plus longues durées (respectivement 6 minutes et 16 xix minutes). Dans l'expérience B les stimuli ont été reproduits par un système 2.1 placé à l'extérieur, face à la fenêtre d'une pièce d'une villa expérimentale. Les participants étaient assis face à cette fenêtre légèrement entrouverte et qui donnait sur un jardin privatif arboré avec une haie végétalisée. Dans l'expérience C, les séquences ont été diffusées par un système de restitution de front d'onde, placé derrière la fenêtre . Les participants étaient installés face à la fenêtre entre ouverte offrant une vue sur une haie végétalisée. Dans chacune de ces 3 expériences, les séquences sonores étaient constituées du bruit de la circulation routière et du bruit du tramway, sur la base des densités de trafic réel observées en France. Dans les expériences A et C, des bruits de fond urbains enregistrés in situ ont été diffusés tout au long de leur déroulement. Dans l'expérience B, la fenêtre de la villa expérimentale était légèrement ouverte et un bruit de fond naturel d'un quartier résidentiel calme était présent.

Les résultats obtenus montrent des tendances similaires concernant les réponses de gêne partielle et de gêne totale. Ainsi ils révèlent que chaque gêne partielle a été influencée par l'autre source de bruit de la combinaison. Pour les expériences A et B, en général, chaque gêne partielle était plus élevée en présence de l'autre bruit présentant des niveaux sonores élevés et des densités du trafic élevées. Cet effet est plus important pour le jugement de la gêne partielle due au bruit du tramway en présence de bruit routier urbain avec une densité de trafic élevé. Par ailleurs, le bruit de la circulation routière a été généralement jugé plus gênant que le bruit du tramway pour un même niveau sonore. En ce qui concerne les réponses de gêne totale obtenues dans les trois expériences, un effet important de la source dominante a été observé. En d'autres termes, la gêne totale était égale aux gênes partielles maximales. Cependant, dans 20% des combinaisons de bruits étudiées de l'expérience A et dans 25 % des combinaisons de bruits de l'expérience C des effets de synergie ont été observés. Dans ces cas-là, les gênes totales étaient supérieures aux gênes partielles maximales. Dans l'expérience B impliquant un bruit de fond naturel dû à la fenêtre légèrement ouverte, le paradoxe des sources de bruits combinées a été observé dans 25% des combinaisons (i.e. une gêne totale inférieure à la gêne partielle maximale). L'effet de paradoxe observé dans l'expérience B a été attribué à la présence du bruit de nature provenant du jardin privatif arboré. En effet, ce résultat a déjà été constaté dans la littérature pour des situations de multi-exposition impliquant des bruits de nature [START_REF] Alayrac | Total annoyance from an industrial noise source with a main spectral component combined with a background noise[END_REF]. Ces derniers tels que les bruits d'oiseaux sont en effet reconnus pour améliorer l'agrément de l'environnement sonore (e.g. [START_REF] Guastavino | The ideal urban soundscape: Investigating the sound quality of French cities[END_REF]).

Le chapitre 6 est dédié au test des modèles prédictifs de gêne totale due au trafic routier urbain combiné au bruit du tramway en utilisant différents modèles de gêne totale établis dans la littérature : différents modèles perceptifs et un modèle psychophysique. Les modèles ont été sélectionnés sur la base des phénomènes mis en évidence précédemment. Les modèles perceptifs ont été testés avec des variables indépendantes sous deux formes : des réponses de gênes partielles mesurées et des réponses de gênes partielles calculées. Les équations des modèles de gêne totale dans ces deux versions ont été construits en utilisant les réponses de gêne de xx Synthèse l'expérience A et leur qualité de prédiction a été évaluée en utilisant les réponses de l'expérience B. Les résultats ont montré la supériorité des modèles perceptifs par rapport au modèle psychophysique, tant du point de vue de l'ajustement des modèles que du point de vue de leur qualité de prédiction.

La comparaison entre modèles perceptifs nous a permis de montrer qu'en utilisant les gênes partielles mesurées, le modèle de source dominante et le modèle mixte permettaient de prédire les réponses de gêne de manière très adéquate. Mais d'un point de vue opérationnel, il serait très avantageux de prédire la gêne totale sur la base des réponses de gênes partielles calculées. Celles-ci pourraient être calculées à partir des enregistrements du bruit de trafic routier et du bruit de la circulation du tramway. La gêne partielle due au bruit du trafic routier urbain a ainsi été calculée en utilisant l'indicateur de gêne proposé dans le chapitre 4 et comprenant N mean , m sputt,10 , m nas,10 et TETC. Ce calcul s'est avéré très approprié lors de sa confrontation à la gêne partielle mesurée lors de l'expérience A. Rappelons que cet indicateur avait été construit pour des bruits de passage de véhicules routiers urbains. Cette confrontation réalisée pour des bruits de trafic routier urbain constitue une validation de l'indicateur pour prédire la gêne due à des bruits de trafic routier urbain pouvant comprendre des deux-roues motorisés. Ensuite, la gêne partielle due au bruit du tramway a été calculée à l'aide de l'indicateur de gêne proposé par Trollé et al. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF][START_REF] Trollé | Corrigendum to short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF] comprenant L Aeq et l'indice TETC. On a ainsi pu observer que parmi les modèles de gêne totale utilisant les gênes partielles calculées, le modèle de source dominante prédit la gêne totale de manière assez juste, malgré une légère surestimation. Une limite du modèle de source dominante est qu'il ne rend pas compte de l'effet de synergie ni de l'effet de paradoxe. Des recherches complémentaires sont nécessaires afin de proposer des modèles perceptifs qui permettent une prédiction adéquate de la gêne totale due à des combinaisons de différentes sources de bruit.

Cette thèse a donc permis de contribuer à l'amélioration de la caractérisation physique et perceptive de la gêne due au bruit du trafic routier urbain en prenant en compte différentes sensations gênantes pertinentes. L'indicateur que nous avons proposé et testé en laboratoire s'est révélé être approprié pour caractériser la gêne due aux bruits de passage de véhicules routiers en milieu urbain et au bruit du trafic routier urbain. Mais, avant une utilisation de cet indicateur sur des données in situ, d'autres confrontations sont nécessaires en laboratoire en considérant un plus grand nombre de trafics routiers urbains.

Une autre perspective à ces travaux est de prendre en compte des facteurs non acoustiques pertinents tels que la sensibilité au bruit. Cette dernière a été mise en évidence dans différentes études in situ [START_REF] Lam | Annoyance response to mixed transportation noise in Hong Kong[END_REF]. Pour pouvoir l'utiliser dans des modèles prédictifs, il faudrait recueillir la sensibilité des personnes au bruit en utilisant des échelles continues allant de "0" (pas du tout sensible) à "10" (extrêmement sensible), comme cela a été menée en laboratoire (e.g. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF][START_REF] Trollé | Corrigendum to short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF]) et au cours d'une enquête in situ (cf. [START_REF] Ecotière | Multi-exposition au bruit des transports -application et évaluation d'une méthode opérationelle d'intification de la gêne en situation de multi-exposition[END_REF]). Ce facteur non acoustique pourrait être utilisé comme variable d'un indicateur de gêne construit grâce à une régression multi-niveaux. Pour prendre en compte in situ différents facteurs acoustiques, la construction de l'indicateur nécessiterait des enregistrements de bruit afin de calculer les valeurs des indices pertinents. Des durées courtes (entre 10 à 20 minutes) peuvent être suffisantes pour caractériser des environnements acoustiques urbains [START_REF] Brocolini | Measurements of acoustic environments for urban soundscapes: Choice of homogeneous periods, optimization of durations, and selection of indicators[END_REF].

Concernant les modèles de gêne totale, nous avons pu démontrer que les modèles perceptifs prédisent la gêne totale due aux bruits combinés mieux que les modèles psychophysiques. Cela souligne la nécessité d'améliorer la caractérisation de la gêne sonore en situation de mono-exposition en prenant en compte les divers facteurs acoustiques et non-acoustiques pertinents grâce à une régression multi-niveaux.

Enfin, ce travail a montré que différents phénomènes perceptifs sont impliqués dans l'expression de la gêne totale due au trafic routier urbain combiné au bruit du tramway et que ceux-ci peuvent affecter la qualité de prédiction des modèles de gêne totale. Afin d'acquérir plus de connaissances sur les phénomènes perceptifs qui prévalent in situ, il est nécessaire d'approfondir l'étude des caractéristiques perceptives impliquées dans la gêne totale due au trafic routier urbain combiné au bruit du tramway. Sur la base de cette connaissance, les modèles perceptifs testés dans ce travail pourraient être affinées pour tenir compte de ces effets.

List of Figures

3.2

The median values for the item "dull/shrill" plotted against the TETC values for the 14 urban road single-vehicle pass-by noises. . . . . . . . .

3.3

The median values for the item "sputtering" plotted against the F max values for the 14 urban road single-vehicle pass-by noises. . . . . . . . .

3.4

The median values for the item "nasal" plotted against the F 10 values for the 14 urban road single-vehicle pass-by noises. . . . . . . . . . . . . .

List of Tables

Introduction

Environmental noise pollution

In the first century BC, the council of a Greek colony named Sybarites passed a noise ordinance banning potters, tinsmiths and other tradesmen from residential areas because of the noise they produced. This is the first documented noise ordinance in history [START_REF] Goldsmith | Discord: The story of noise[END_REF]. In his third Satire (117 AD) the poet Juvenile said: "It is absolutely impossible to sleep anywhere in the city. The perpetual traffic of wagons in the surrounding streets (...) is sufficient to wake the dead" [START_REF] Schafer | The soundscape: Our sonic environment and the tuning of the world[END_REF]. While this extract dates back thousands of years, it suggests that noise has been a part of the ancient society. In an attempt to reduce the ever-increasing noise pollution in cities, numerous noise-related ordinances were passed between the thirteenth and nineteenth century restricting for instance blacksmiths to special areas or interdicting the barking of dogs [START_REF] Schafer | The soundscape: Our sonic environment and the tuning of the world[END_REF].

Due to technological progress and cultural change, the characteristics of noises in cities have nowadays transformed. The prevalence of noise, however, remains an inherent part of our society. Today, in urban areas, people are exposed to a wide range of environmental noises which can be experienced separately or in combination. These are noises of transport (road, rail and air traffic), construction and industry, community sources (e.g. neighbours, radio and television) and social and leisure sources (e.g. portable music players, fireworks) [124]. Generally, due to ongoing urbanization more people are affected by noise. For local authorities, administrators and policy makers, as well as for the public, environmental noise pollution is a growing concern. According to a recent European survey, more than 44 % of the respondents believe that noise affects human health to a great extent which corresponds to a 3 % increase since 2006 [START_REF][END_REF]. Health, as defined by the WHO, is a state of complete physical, mental, social well-being [124]. Noise exposure can lead to cardiovascular disease, cognitive impairment, sleep disturbance, tinnitus and annoyance [START_REF] Passchier-Vermeer | Noise and health[END_REF]124]. One of the main effects of noise exposure is annoyance. A national survey conducted in 1989 showed that between 35 to 40 % of the French population feel annoyed by noises in their domestic environments. Transportation noises were found to contribute mostly (55 %) and in particular powered-twowheelers (19 %), light vehicles (17 %) and heavy vehicles (11 %) [START_REF] Dufour | Perception du cadre de vie et attitudes sur l'environnement[END_REF]. In a more recent French survey 51 % of respondents declared to be annoyed by noise, 66 % feel mostly annoyed by road traffic noise and 45 % by noise from powered-two-wheelers [START_REF] Roy | La sensibilité des français à leur environnement de proximité[END_REF]. In support of that, it has been shown that the presence of public transport at daytime and at night is a significant predictor for high noise annoyance in an urban area [START_REF] Paunović | Noise annoyance is related to the presence of urban public transport[END_REF].

Noise mapping in urban areas

The European Parliament and Council adopted the Directive 2002/49/EC (Environmental Noise Directive, END) in 2002 relating to the assessment and management of environmental noise. One of the main objectives of the END is to establish a common basis for assessing noise exposure in the EU. The END requires European cities of more than 100.000 inhabitants to represent community noise in the form of strategic noise maps for major roads, railways, airports and agglomerations. Strategic noise maps represent the exposure to environmental noise within certain areas of interest. They are based on measurements and predictive noise models and represent community noise by means of the indices L den (day-evening-night level) and L night (night equivalent level). To relate annoyance due to transportation noise sources to the L den index, exposure-response relationships have been created [START_REF] Schultz | Synthesis of social surveys on noise annoyance[END_REF][START_REF] Miedema | Exposure-response relationships for transportation noise[END_REF]. Noise maps can, thus be interpreted as maps representing citizens' annoyance potential.

Various studies showed, however, that only about one third of the annoyance responses can be explained by this energy-based index, casting doubt on its validity (e.g. [START_REF] Berglund | Community noise in a public health perspective[END_REF][START_REF] Job | Community response to noise: a review of factors influencing the relationship between noise exposure and reaction[END_REF]). Sound characteristics of transportation noises linked to temporal and spectral noise features have been found to influence noise annoyance responses (e.g. [START_REF] Ishiyama | The impact of sound quality on annoyance caused by road traffic noise: an influence of frequency spectra on annoyance[END_REF][START_REF] Morel | Physical and perceptual characterization of road traffic noises in urban areas for a better noise annoyance assessment[END_REF][START_REF] Paviotti | On the outdoor annoyance from scooter and motorbike noise in the urban environment[END_REF]). Especially noise emissions from powered-two-wheelers (e.g. motorcycles, mopeds) have become a major concern in urban areas as their use increased significantly over the last decade [START_REF] Lercher | Exposure by motorbike noise in alpine residential areas -a case study in public health risk assessment[END_REF]. Among other road traffic vehicles, powered-two-wheelers were found to elicit the highest annoyance reactions (e.g. [START_REF] Berglund | Total annoyance and perceptually discernible noise sources[END_REF][START_REF] Paviotti | On the outdoor annoyance from scooter and motorbike noise in the urban environment[END_REF]). This may be due to the fact that powered-two-wheelers exhibit specific temporal characteristics relating to amplitude modulations [START_REF] Paviotti | On the outdoor annoyance from scooter and motorbike noise in the urban environment[END_REF].

To date, most EU member states address two categories of vehicles in the noise mapping process: light and heavy vehicles. In an attempt to distinguish between different vehicle types in the noise mapping process, a common approach has been recently proposed to group road traffic vehicles according to their noise emission characteristics. Four main categories are introduced (light motor vehicles, medium heavy vehicles, heavy vehicles, mopeds and motorcycles) and one additional "open" category to account for newly developed vehicles, such as hybrid cars [START_REF] Kephalopoulos | Advances in the development of common noise assessment methods in europe: The cnossos-eu framework for strategic environmental noise mapping[END_REF]. The inclusion of powered-two-wheelers in strategic noise mapping is a progress, however, as the L den index is energy-based, it is not capable to account for the annoyance-relevant spectral and temporal features of powered-two-wheelers. Thus, in order to enhance the characterization of annoyance due to urban road traffic noise, an adequate perception-related characterization of influential acoustical features is essential, especially focusing on the characterization of powered-twowheelers.

According to the END, noise maps are produced separately for each traffic noise source. In urban areas, however, road traffic noise is usually not heard separately but in combination with other noises. These noise sources may interact with each other causing higher or lower annoyance responses as showed in recent studies (e.g [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF][START_REF] Lercher | The effects of noise from combined traffic sources on annoyance: the case of interactions between rail and road noise[END_REF]). Understanding the effects from human exposure to urban road traffic noise combined with other environmental noise sources is essential for the development of models which can be used to characterize annoyance perceived by people living in urban areas.

Outline

This thesis aims to contribute to the improvement of noise annoyance assessment in urban areas by improving the physical and perceptual characterization of annoyance due to urban road traffic noise and tramway noise. It is concerned in the first instance with the improvement of the physical and perceptual characterization of annoyance due to urban road single-vehicle pass-by noises. The objective is to identify and characterize annoyance-relevant spectral and temporal features. The focal points of the second part is i) on the understanding of perceptual phenomena involved in total annoyance due to urban road traffic noise combined with tramway noise, and ii) on the characterization of total annoyance due to combined urban road traffic and tramway noises.

Chapter 1 provides a review of literature dealing with annoyance due to road traffic noise and tramway noise both in single and combined noise exposure. The literature review first covers findings from literature regarding single noise exposure experiments. Then, it covers fundamental definitions relating to combined noise exposure. State-of-the-art total annoyance models used in existing literature are defined and evaluated based on literature findings. The perceptual phenomena involved in total annoyance due combined noise sources are reviewed. Chapter 2 builds on current research and aims at improving the characterization of annoyance due to urban road vehicle pass-by noises through the application of spectral and temporal indices proposed in recent literature. First, spectral and temporal indices are tested using annoyance responses obtained by Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF]. Second, two different roughness models are tested for improved characterization of annoyance responses. In order to improve the characterization of annoyance due to urban road vehicle pass-by noises, an experiment is carried which allows to identify and characterize annoyance-relevant auditory features. The experiment is detailed in Chapter 3. The chapter starts with the experimental methodology of a semantic differential test combined with a verbalization task. Adapted spectral and temporal indices characterizing the annoyance-relevant sensations are then developed. The proposed indices are tested based on two other experiments involving a broad range of urban road vehicle pass-by noises in Chapter 4. This chapter will lead to the proposal of annoyance indicators for the characterization of annoyance due to urban road single-vehicle pass-by noises.

The objective of the following chapters is to test the proposed indicator for the characterization of annoyance due to urban road traffic noise combined with tramway noise. For the characterization of annoyance due to tramway noise, this work will benefit from a characterization carried out in a recent study (cf. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF][START_REF] Trollé | Corrigendum to short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF]). First, to gain a thorough understanding of the perception-related effects involved in total annoyance due to road traffic heard in presence of tramway traffic noise, Chapter 5 presents the experimental methodologies and results of three experiments: two carried out in the framework of this thesis and one conducted within the project VIA URBA. The observed phenomena are compared across these experiments and also with findings from literature. Chapter 6 deals with the prediction of total annoyance based on selected total annoyance models from literature. The chapter starts with a short description of these relevant total annoyance models. Before proposing models for the prediction of total annoyance due to combined road traffic and tramway traffic, both road traffic and tramway traffic partial annoyances are characterized. This is followed by the construction and testing of the total annoyance models to determine relevant total annoyance models for the prediction of noise annoyance due to road traffic noise combined with tramway noise.

Chapter 1

Review of relevant literature

Background

Adverse effects of noise, such as noise annoyance, have been extensively studied over the last decades. However, Guski et al. [START_REF] Guski | The concept of noise annoyance: How international experts see it[END_REF] revealed wide differences in the understanding of noise annoyance across several studies. In order to find consensus in the definition of noise annoyance, they conducted a cross-cultural study involving 68 noise research experts. They define noise annoyance as a psychological concept which describes a relation between an acoustic situation and a person who is forced by noise to do things he/she does not want to do, who cognitively and emotionally evaluates this situation and feels partly helpless. Thus, by definition the concept of noise annoyance is complex involving the relationship between the noise source and the person. The definition of "unpleasantness", for instance, implies only an instantaneous evaluation of the pleasant or unpleasant character of a sound [START_REF] Guski | Psychological methods for evaluating sound quality and assessing acoustic information[END_REF].

Short-term noise annoyance: Short-term noise annoyance refers to primary reactions to noise and is usually assessed in laboratory conditions. In their laboratory experiments Berglund et al. [START_REF] Berglund | Scaling loudness, noisiness and annoyance of aircraft noise[END_REF] defined short-term annoyance as the nuisance aspect of the noise experienced in an imaginary situation. This notion has been adapted by several researchers which studied short-term annoyance due to different noise sources in imaginary or simulated context (e.g. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF][START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF]). The imaginary or simulated context allows real-life situations to be activated in memory; annoyance judgments are thus broader than unpleasantness judgments. The aim of such laboratory experiments is to control noise exposure in order to investigate influential acoustical features with regard to annoyance.

Long-term noise annoyance: Long-term noise annoyance as assessed in field studies is regarded as a long-term effect of noise. Usually, respondents are asked at their homes to provide a retrospective annoyance judgment. The standardized annoyance question for the use in questionnaires is given by Fields et al. [START_REF] Fields | Standardized general-purpose noise reaction questions for community noise survey: Research and recommendation[END_REF]: "Thinking about the last (12 months or so), when you are here at home, how much does this noise bother, disturb, or annoy you". Annoyance responses obtained in field studies reflect actual annoyance involving acoustical and non-acoustical factors. These acoustical and nonacoustical factors will be reviewed in the following section.

Acoustical factors influencing noise annoyance

Transportation noise-induced annoyance is considered the most serious problem impacting peoples' well-being in urban environments. In the following, acoustical factors influencing noise annoyance due to road traffic and to tramway noises are reviewed as these noise sources will be studied in this thesis.

Road traffic noise annoyance

Urban road traffic is composed of various types of vehicles such as light vehicles, buses, trucks and powered-two-wheelers. These vehicles emit noises which may precipitate different annoyance reactions. Several studies reported that poweredtwo-wheelers often elicit higher annoyance reactions than other road traffic vehicles (e.g. [START_REF] Paviotti | On the outdoor annoyance from scooter and motorbike noise in the urban environment[END_REF][START_REF] Berglund | Total annoyance and perceptually discernible noise sources[END_REF][START_REF] Sandberg | Noise emission from powered two-wheeled vehicles -position paper[END_REF][START_REF] Vos | Noise annoyance caused by mopeds and other traffic sources[END_REF][START_REF] Lercher | Exposure by motorbike noise in alpine residential areas -a case study in public health risk assessment[END_REF]). For instance, Berglund and Nilsson [START_REF] Berglund | Total annoyance and perceptually discernible noise sources[END_REF] analyzed annoyance responses from a field study for different vehicle types, such as light vehicles, heavy vehicles and motorcycles, and found that motorcycles are the most annoying noise sources. Furthermore, results from a national survey conducted in the Netherlands (Franssen et al. [START_REF] Franssen | Hinder door milieufactoren en de beoordeling van de leefomgeving in nederlandinventarisatie verstoringen[END_REF] cited in Vos [START_REF] Vos | Noise annoyance caused by mopeds and other traffic sources[END_REF]), showed that mopeds1 yielded the highest percentage of "highly annoyed" respondents.

Despite the considerable increase in the use of powered-two-wheelers over the last decade [START_REF] Lercher | Exposure by motorbike noise in alpine residential areas -a case study in public health risk assessment[END_REF], only a few studies, dealing with annoyance due to road traffic noise, considered these characteristic urban road traffic vehicles. Paviotti and Vogiatzis [START_REF] Paviotti | On the outdoor annoyance from scooter and motorbike noise in the urban environment[END_REF] conducted a study to investigate differences in acoustical features between powered-two-wheelers and light vehicles to better explain annoyance responses. They showed that powered-two-wheelers are highly annoying specifically for pedestrians when low background noise levels and sparse traffic flow allow identifying the source [START_REF] Paviotti | On the outdoor annoyance from scooter and motorbike noise in the urban environment[END_REF]. Langdon [START_REF] Langdon | Noise nuisance caused by road traffic in residential areas: Part II[END_REF] conducted a field study investigating the influence of non-free flow traffic on noise nuisance. He found that taking into account the proportion of heavy vehicles resulted in improved prediction of nuisance. Similar results were also found by Labiale in laboratory conditions [START_REF] Labiale | Laboratory study of the influence of noise level and vehicle number on annoyance[END_REF]. He showed that annoyance is influenced by the level and by the number of truck passages. Some studies, however, indicated that annoyance may be independent of the proportion of heavy vehicles (e.g. [START_REF] Versfeld | A-weighted sound level as a predictor of the annoyance caused by road traffic consisting of various proportions of light and heavy vehicles[END_REF][START_REF] Griefahn | The significance of traffic flow and traffic composition for annoyance and performance[END_REF]). Several attempts have been made to characterize the annoyance evoked by different urban road vehicle pass-by noises.

Throughout different studies various energy-based indices, such as L den , L day , L evening , L night and L Aeq , have been used to characterize annoyance due to road traffic noise. For instance, Job [START_REF] Job | Community response to noise: a review of factors influencing the relationship between noise exposure and reaction[END_REF] reviewed in situ studies across different nationalities with different measurement techniques on noise annoyance due to road traffic noise and concluded that noise exposure accounts for only about 20 % of the variation in individual annoyance responses. For Berglund [START_REF] Berglund | Community noise in a public health perspective[END_REF] 30 % of annoyance reactions could be explained using the A-weighted equivalent sound pressure level (L Aeq ). In a recent field study, Pierrette et al. [START_REF] Pierrette | Noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF][START_REF] Pierrette | Corrigendum to noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF] showed that different indices describing road traffic noise (L den , L day , L evening , L night ) explained only between 38 and 45 % in the variation of the annoyance judgments. This shows that noise annoyance due to road traffic noise cannot solely be predicted by energy-related indices. Hence, to improve noise annoyance assessment, other acoustical noise features influencing noise annoyance judgments should be considered.

Concerning spectral signal features, Ishiyama and Hashimoto [START_REF] Ishiyama | The impact of sound quality on annoyance caused by road traffic noise: an influence of frequency spectra on annoyance[END_REF] showed that the high-frequency content of road traffic noise significantly contributes to noise annoyance. Single pass-by noises with modified frequency spectra above 1 kHz were studied. It was shown that single pass-by noises with the same L Aeq but larger high-frequency components cause higher annoyance. Morel et al. [START_REF] Morel | Physical and perceptual characterization of road traffic noises in urban areas for a better noise annoyance assessment[END_REF] studied in situ recorded urban road vehicle pass-by noises and proposed to use specific loudness integrated over 15 and 18 Barks in order to take into account high frequency content influencing annoyance due to urban road single-vehicle pass-by noises.

Regarding temporal signal features of transportation noise and specifically road traffic noise, several studies demonstrated the importance of characterizing irregular envelope variations in terms of annoyance (e.g. [START_REF] Vos | Noise annoyance caused by mopeds and other traffic sources[END_REF][START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF]). Generally, it was shown that noises composed of regular amplitude modulations tend to be more annoying than noises with irregular amplitude variations [START_REF] Kantarelis | The identification and subjective effect of amplitude modulation in diesel engine exhaust noise[END_REF]. A few studies investigated the effect of amplitude modulations inherent in urban road traffic noise on noise annoyance. For different urban road vehicle pass-by noises, such as powered-two-wheelers, heavy vehicles, buses and light vehicles, the psychoacoustic indices roughness and fluctuation strength were shown to be correlated only to a certain degree with noise annoyance [START_REF] Paviotti | On the outdoor annoyance from scooter and motorbike noise in the urban environment[END_REF][START_REF] Morel | Physical and perceptual characterization of road traffic noises in urban areas for a better noise annoyance assessment[END_REF]. Paviotti and Vogiatzis [START_REF] Paviotti | On the outdoor annoyance from scooter and motorbike noise in the urban environment[END_REF] stated that amplitude modulations present in powered-two-wheeler pass-by noises potentially influence noise annoyance. They concluded that the roughness index is not capable to account for noise annoyance evoked by powered-two-wheelers. This shows that there is a lack of indices to adequately characterize annoyance due to these vehicles.

Studying annoyance due to road traffic noises, Fastl and Zwicker [START_REF] Fastl | Psychoacoustics -Facts and Models[END_REF] postulate that annoyance can be characterized by a combination of psychoacoustic indices. This indicator called "psychoacoustic annoyance" is a combination of loudness, sharpness, fluctuation strength and roughness. An experiment carried out to test the relevance of this indicator using pass-by noises from light vehicles in different driving conditions (constant speed, idling, coasting, acceleration) and at different speeds (between 30 and 110 km/h) showed a good predictive potential [START_REF] Fastl | Psychoacoustics -Facts and Models[END_REF]. Other studies also tested this indicator but questioned its applicability (e.g. [START_REF] Kaczmarek | Annoyance of time-varying road-traffic noise[END_REF]).

Tramway noise annoyance

Only very few studies have been performed dealing with annoyance due to tramway noise [START_REF] Pallas | Bruit et vibrations dus aux tramways[END_REF][START_REF] Sandrock | Experimental studies on annoyance caused by noises from trams and buses[END_REF][START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF]. Sandrock et al. [START_REF] Sandrock | Experimental studies on annoyance caused by noises from trams and buses[END_REF] conducted laboratory experiments in order to compare annoyance from tramway noises and buses. They found evidence for a tramway bonus, as the tramway was equally annoying as the bus with at a 3 dB lower level. Furthermore, they highlighted that the psychoacoustic features loudness and sharpness could be responsible for differences in annoyance responses between the two noises. Pallas et al. [START_REF] Pallas | Bruit et vibrations dus aux tramways[END_REF] compared different regression models to explain annoyance responses from tramway pass-bys obtained in an in situ study. They proposed a perceptual model comprising an energy-based index (L Amax or SEL) and variables such as "short/long", "bass/treble", "distant/nearby" (items used in semantic differential) and rumbling, squealing (number of occurrences of terms describing the tramway noise). This model was found to yield a greater predictive power than a psychophysical model only based on an energy-based index. They highlighted the importance accounting for annoyance-relevant frequency components in characterizing annoyance due to tramway noise.

With the objective to characterize short-term annoyance due to tramway noises, Trollé et al. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF] carried out an experiment in laboratory conditions and also highlighted the relevance of spectral features. Free verbalizations collected after the experiment revealed that the bass/treble character of the tramway noises likely influenced short-term annoyance. The TETC index was proposed taking into account the energy of tonal components in high frequencies. A noise annoyance indicator constructed using the A-weighted equivalent sound pressure level and the proposed spectral index proved to be adequate to account for noise annoyance.

Non-acoustical factors influencing noise annoyance

The studies previously reviewed suggest that in addition to acoustical features noise-induced annoyance may be influenced by non-acoustical factors. Based on a meta-analysis, Fields [START_REF] Fields | Effect of personal and situational variables on noise annoyance in residential areas[END_REF] identified three main categories of variables which may impact noise annoyance:

• Demographic variables: sex, social status, income, education, home ownership, dwelling type, length of residence, benefit from the noise source (e.g. employees and users of the noise source), • Attitudinal variables: fear, preventability, non-noise annoyance, sensitivity, importance (i.e. the belief that the noise source is important), • Situational variables: exposure time, insulation, ambient noise.

He found that annoyance is affected by the amount sound isolation at home and to the five attitudinal variables fear of danger from the noise source, noise prevention beliefs, general noise sensitivity, beliefs about the importance of the noise source, and annoyance with non-noise impacts of the noise source. This topic is beyond the scope of this thesis. For an extensive overview of the non-acoustical factors influencing noise annoyance both in situ and in laboratory conditions, the reader may refer to Marquis-Favre et al. [START_REF] Marquis-Favre | Noise and its effects -a review on qualitative aspects of sound. Part II: Noise and annoyance[END_REF], Job [START_REF] Job | Community response to noise: a review of factors influencing the relationship between noise exposure and reaction[END_REF] and Lambert and Philipps-Bertin [START_REF] Lambert | Moderators in noise annoyance studies: an overview[END_REF].

Noise annoyance due to combined noise exposure

In urban environments the exposure of people to only one type of transportation noise source is rare. These complex environments usually present noises from different types of sources (neighborhood noise, road noise, air conditioner noise, tramway noise, aircraft noise, etc.). For instance, generally tramway noises are experienced simultaneously with road traffic noise. In current literature different terms are employed to describe noise exposure to a combination of noises, such as "mixed noise sources", "simultaneous noise sources" and "combined noise sources". Throughout this work, the term "combined noise sources" will be used.

Champelovier et al. [START_REF] Champelovier | Evaluation de la gêne due à l'exposition combinée aux bruits routier et ferroviaire[END_REF] defined combined noise exposure as the exposure to several environmental noise sources. These noise sources cannot be compared with background noise as the sources are clearly identifiable from an acoustical, a visual and a perceptual point of view. Combined noise exposure is different from single noise-exposure as it refers to several sources as opposed to just one source.

Different notations are employed when referring to combined noise exposure; these are described in the following (e.g. [START_REF] Berglund | Empirical issues concerning annoyance models for combined community noises[END_REF][START_REF] Berglund | Loudness of combined noises derived from singular and concurrent community noises[END_REF][START_REF] Miedema | Relationship between exposure to multiple noise sources and noise annoyance[END_REF]).

Total annoyance: Total annoyance also labelled "overall annoyance" or "global annoyance", refers to the annoyance caused by a combination of noises.

Specific annoyance:

The annoyance evoked by one source heard in isolation is called specific annoyance. Specific annoyance corresponds to the annoyance measured in single noise exposure.

Partial annoyance: Introduced by Berglund and Nilsson [START_REF] Berglund | Empirical issues concerning annoyance models for combined community noises[END_REF], the term partial annoyance refers to the annoyance evoked by one source which is heard in combination with other sources. It must be noted that both specific annoyance and partial annoyance are used in laboratory and in in situ studies (e.g. [START_REF] Taylor | A comparison of models to predict annoyance to noise from mixed sources[END_REF]). However, specific annoyance measured in situ should be interpreted with caution as it is difficult to measure noise sources isolated in real environments. Thus, in some studies specific annoyance actually corresponds more to partial annoyance (cf. [START_REF] Berglund | Empirical issues concerning annoyance models for combined community noises[END_REF]).

When conducting combined noise exposure experiments in laboratory conditions, some methodical specifics should be considered. For the construction of the combined noise stimuli, the choice of the noises and the noise levels is an important aspect [START_REF] Berglund | Empirical issues concerning annoyance models for combined community noises[END_REF]. Berglund and Nilsson [START_REF] Berglund | Empirical issues concerning annoyance models for combined community noises[END_REF] recommend to study all unique combinations of at least five noise levels of each noise (i.e. 5 x 5 matrix) . In literature, a few studies considered all the combinations of the noise matrix for the construction of their stimuli (e.g. [START_REF] Flindell | Community Response to multiple noise sources[END_REF]); most studies only consider certain elements of this matrix (e.g. [START_REF] Berglund | Loudness (or annoyance) summation of combined community noises[END_REF][START_REF] Vos | Annoyance caused by simultaneous impulse, road-traffic, and aircraft sounds: a quantitative model[END_REF]). In field studies, the noise combinations are fixed as the noises exist in the environment at the levels they happen to be [START_REF] Berglund | Empirical issues concerning annoyance models for combined community noises[END_REF].

Before presenting the models for the prediction of annoyance due to combined noises, the perceptual phenomena involved in annoyance due to combined noise exposure will be described.

Perceptual phenomena related to annoyance due to combined noise exposure

In the following an overview of the different perceptual phenomena observed across various types of combined noises is given.

Strongest component

A source is called strongest component, when the specific or partial annoyance of this source is greater compared to the specific or partial annoyance of other sources presented in combination. For certain combined noise situations, the total annoyance is equal to the maximal specific or partial annoyance. The model describing this effect has been labelled strongest component or dominant sources model [START_REF] Botteldooren | Fuzzy models for accumulation of reported community noise annoyance from combined sources[END_REF].

The strongest component effect has been found in various studies (e.g. [START_REF] Vos | Annoyance caused by simultaneous impulse, road-traffic, and aircraft sounds: a quantitative model[END_REF][START_REF] Ronnenbaum | Synergetic effects of noise from different sources: A literature study[END_REF][START_REF] Berglund | Loudness (or annoyance) summation of combined community noises[END_REF][START_REF] Botteldooren | Fuzzy models for accumulation of reported community noise annoyance from combined sources[END_REF]). Vos [START_REF] Vos | Annoyance caused by simultaneous impulse, road-traffic, and aircraft sounds: a quantitative model[END_REF], for example, studied total annoyance from road traffic, gunfire and aircraft noise. In general, he found that when the annoyance of one source was considerably higher than the annoyance of another source, total annoyance was equal to the maximum annoyance of the separate sources suggesting the presence of the strongest component effect.

Synergy

Sometimes in both in situ and laboratory studies, total annoyance due to the combination of two noises is found to be greater than the maximal specific or partial annoyance of one source (e.g. [START_REF] Ronnenbaum | Synergetic effects of noise from different sources: A literature study[END_REF][START_REF] Berglund | Loudness (or annoyance) summation of combined community noises[END_REF][START_REF] Taylor | A comparison of models to predict annoyance to noise from mixed sources[END_REF]). This effect has been referred to as a synergistic effect (e.g. [START_REF] Berglund | Guidelines for community noise[END_REF]).

For instance, Ota et al. [START_REF] Ota | The metrics of mixed traffic noise: results of simulated environments experiments[END_REF] conducted a laboratory experiment in addition to a field study [START_REF] Ota | Comparison of dose-response relationships among different traffic noises -social survey on residential areas exposed to combined noises[END_REF] and found a synergistic effect when partial annoyances of both road traffic and railway noise were approximately the same. In a field study Öhrström et al. [START_REF] Öhrström | Annoyance due to single and combined sound exposure from railway and road traffic[END_REF] found that in areas exposed to both high levels of road traffic and railway noise total annoyance was found to be significantly higher compared to areas exposed to only one dominant source (road traffic or railway noise).

Inhibition

Inhibition between two noise sources prevails, when the annoyance of one source in presence of the other source is reduced relative to the annoyance of the first noise measured in single noise exposure. Early combined noise studies found evidence for interaction effects between the noises in combination.

Powell and Rice [START_REF] Powell | Judgements of aircraft noise in a traffic noise background[END_REF], for example, studied annoyance from aircraft noises presented in a road traffic background and found that an increase in the sound pressure level of the road traffic noise resulted in a decrease of the aircraft annoyance. According to Powell [START_REF] Powell | A summation and inhibition model of annoyance response to multiple community noise sources[END_REF], inhibition of two stimuli presented simultaneously is caused by spectral masking. Berglund and Nilsson [START_REF] Berglund | Total annoyance models for community noises explicated[END_REF] point out that highway road traffic noise is a more efficient masker than train noise in the combinations they studied. Based on observations of spectral masking and the effects on annoyance judgments, Morel et al. [START_REF] Morel | A laboratory study on total noise annoyance due to combined industrial noises[END_REF] introduced the power of inhibition of one source over another source. It describes the capability of one source to become the strongest component when combined together. For combined road traffic and railway noise, Izumi [START_REF] Izumi | Annoyance due to mixed source noises -a laboratory study and field survey on the annoyance of road traffic and railway noise[END_REF] and Lam et al. [START_REF] Lam | Annoyance response to mixed transportation noise in Hong Kong[END_REF] found that road traffic annoyance was inhibited by the presence of railway annoyance. For Izumi this was true especially for high sound pressure levels of railway noise.

Combined noise sources paradox

Both in in situ and in laboratory studies, total annoyance is often found to be smaller than the maximal specific or partial annoyance. In literature, different terms have been used relating to this effect: "combined noise sources paradox" [START_REF] Hatfield | Clarifying "soundscape": Effects of question format on reaction to noise from combined sources[END_REF] and "principle of compromise" [START_REF] Berglund | Empirical issues concerning annoyance models for combined community noises[END_REF]. Some authors considered this effect as a methodological artifact relating, for example, to the order of the annoyance questions in the questionnaire (e.g. [START_REF] Schulte-Fortkamp | Overall annoyance ratings in a multisource environment[END_REF]). Others (e.g. [START_REF] Hatfield | Clarifying "soundscape": Effects of question format on reaction to noise from combined sources[END_REF][START_REF] Miedema | Relationship between exposure to multiple noise sources and noise annoyance[END_REF]) assume that the paradox may be a real effect of the integration of different noises at the acoustical, auditory and/or psychological level. According to Job and Hatfield [START_REF] Job | Factors contributing to reported reaction to combined noise sources[END_REF], the paradox may be related to different interpretations of the total annoyance question. The total annoyance question may be interpreted in such a manner that total annoyance ratings reflect the time the combined noises are present at the same time. This is usually a rare case which could result in low total annoyance ratings. Total annoyance ratings may also reflect the average response to various noise sources in combination. Berglund and Nilsson [START_REF] Berglund | Empirical issues concerning annoyance models for combined community noises[END_REF] indicated that averaged total annoyance ratings could fit some total annoyance data. Moreover, respondents may also interpret the total annoyance question to judge annoyance over the total duration of the sequence comprising quiet times. The focus on these quiet times may thus reduce the total annoyance ratings.

Alayrac [START_REF] Alayrac | Indicateurs de gêne sonore pour l'étude d'impact du bruit d'un site industriel : caractérisation physique et perceptive[END_REF] studied industrial noise combined with natural noise constituted of birdsong and water noises. They attributed this cognitive effect to the presence of natural noise. As found in several studies, natural noise evokes positive judgments and is known to enhance the pleasantness of the sound environment (e.g. [START_REF] Guastavino | The ideal urban soundscape: Investigating the sound quality of French cities[END_REF]).

In certain studies, several of the aforementioned perceptual phenomena occurred. For instance, Ota et al. [START_REF] Ota | The metrics of mixed traffic noise: results of simulated environments experiments[END_REF] found a strongest component effect when partial annoyance due to road traffic noise was the highest. Furthermore, when partial annoyance due to railway noise was the greatest, they also observed the combined noise sources paradox. This illustrates the complexity of combined noise source situations and emphasizes the need to gain a better understanding of such perceptual phenomena to adequately predict total noise annoyance.

Noise annoyance due to combined road traffic and tramway noises

Few studies addressed the issue of noise annoyance due to combined road traffic and tramway noise. Rylander et al. [START_REF] Rylander | Tramway noise in city traffic[END_REF] conducted a field study on annoyance due to tramway noise and found that people are capable to distinguish between road traffic noise and tramway noise with respect to annoyance. Moreover, measured dose-response relationships were found to be different for both noise sources. Annoyance due to road traffic noise was found to increase more with an increasing number of heavy vehicles than annoyance due to tramway noise. They concluded that the difference in acoustical characteristics between road traffic and tramway traffic may play an important role. Miedema et al. [START_REF] Miedema | Community response to tramway noise[END_REF] conducted an in situ study and highlighted the contribution of tramway noise to total annoyance due to combined road traffic and tramway noises. For 76 % of interviewed persons, tramway noise annoyance was above or equal to road traffic annoyance. If the tramway annoyance was substantially below the road traffic annoyance, it appeared as if tramway noise did not contribute to total annoyance. They also concluded that specific acoustical characteristics of tramway noise such as squeal noise may have an impact on annoyance. Alim and Zaki [START_REF] Alim | Subjective and objective assessments of tram noise in city Alexandria[END_REF] carried out a study to investigate annoyance reactions of residents living in buildings facing tramway tracks. They found weak correlations between the annoyance responses of the residents and the noise exposure (L eq ). Vogt et al. [START_REF] Vogt | Simulation of tram noise abatement procedures in the psychoacoustic laboratory: A pilot study[END_REF] carried out laboratory experiments to investigate the effect of different tramway technologies on noise annoyance but no significant differences could be identified. Paunović et al. [START_REF] Paunović | Noise annoyance is related to the presence of urban public transport[END_REF] conducted a field study in Belgrad with the objective to study noise annoyance in an urban population due to the presence of public transport vehicles such as buses, tramways and trolleybuses. They point out that at night time, the combination of buses and tramways is considered the most annoying type of public transport.

Models for annoyance due to combined noise exposure

Several total annoyance models have been proposed in existing literature for combined noises (a summary can be found in Marquis-Favre et al. [START_REF] Marquis-Favre | Noise and its effects -a review on qualitative aspects of sound. Part II: Noise and annoyance[END_REF]). In general, total annoyance models can take two forms; they can be either psychophysical or perceptual depending on whether these models use psychophysical or perceptual variables. Many studies have been performed testing these models with different combinations of noises (e.g. [START_REF] Ronnenbaum | Synergetic effects of noise from different sources: A literature study[END_REF][START_REF] Berglund | Loudness (or annoyance) summation of combined community noises[END_REF]). Five different models were selected based on their goodness-of-fit or prediction quality compared to other tested models. They are presented hereafter.

Weighted summation model

This model translates the noise from the individual sources into equally annoying sound energy levels of a reference source and then sums these levels. Both Vos [START_REF] Vos | Annoyance caused by simultaneous impulse, road-traffic, and aircraft sounds: a quantitative model[END_REF] and Miedema [START_REF] Miedema | Relationship between exposure to multiple noise sources and noise annoyance[END_REF] selected road traffic as their reference source. For two noises in combination the general model takes the following form:

L t = k • log 10 10 L Aeq,ref k + 10 L Aeq,2 +P 2 k dB(A) (1.1)
P 2 = a 2 -a 1 + (b 2 -b 1 ) • L Aeq,2 b 1 (1.2)
where L t denotes the equivalent overall or total sound level [START_REF] Vos | Annoyance caused by simultaneous impulse, road-traffic, and aircraft sounds: a quantitative model[END_REF] and P 2 the leveldependent penalty of the combined noises with respect to the reference noise. L Aeq,ref is the sound pressure level of the reference source and L Aeq,2 the sound pressure level of the second source. The parameter k is a free parameter which must be determined. The regression coefficients a 1 , a 2 , b 1 and b 2 are obtained from the regression models between the respective sound pressure levels of the reference source and of the second source and their annoyance ratings.

A ref = a 1 + b 1 • L Aeq,ref (1.3)
A 2 = a 2 + b 2 • L Aeq,2 (1.4)
Total annoyance is obtained using the regression equation of the reference source and the equivalent total sound level L t (Eq. 1.1).

A T = a 1 + b 1 • L t (1.5)
Concerning this model, Marquis-Favre et al. [START_REF] Marquis-Favre | Noise and its effects -a review on qualitative aspects of sound. Part II: Noise and annoyance[END_REF] highlighted that the choice of a common unit to express annoyance, (i.e. converting a type of noise into "equivalent" annoyance) is useful, and may enable the comparison and combination of noises. Morel et al. [START_REF] Morel | Annoyance due to combined road traffic and industrial noises: a simulated environment experiment[END_REF] tested different psychophysical models and the weighted summation model predicted total annoyance most adequately. For road traffic combined with railway noises, this model found support by Kaku et al. [53]. This model is the basis for the annoyance equivalents model [START_REF] Miedema | Relationship between exposure to multiple noise sources and noise annoyance[END_REF] recommended by the EU for the assessment of annoyance due to different noise sources in relation with dose-response relationships [START_REF] Vos | Noise maps in the european union: an overview[END_REF].

After introducing the total annoyance model proposed by Vos [START_REF] Vos | Annoyance caused by simultaneous impulse, road-traffic, and aircraft sounds: a quantitative model[END_REF], it is considered important to show the method adopted by Vos for representing total and specific annoyance ratings of combined noise sources. By fixing the sound pressure level of a source of one combination, it allows for the direct comparison between total annoyance and the source specific annoyances as a function of the sound pressure levels. Vos [START_REF] Vos | Annoyance caused by simultaneous impulse, road-traffic, and aircraft sounds: a quantitative model[END_REF] conducted a study on annoyance due to road traffic (T), gun fire (G) and aircraft noise (A). In this particular case, Vos concluded that when one noise in the combination was more annoying than the other noise, total annoyance was close to the rating of the more annoying noise.

Strongest component model

The strongest component model states that total annoyance is equal to the maximal specific or partial annoyance of the noise combination.

A T = max i=1,n (A i ) (1.6)
This model proved to predict total annoyance responses adequately in situ throughout different combinations of noises (e.g. for road traffic combined with railway noise [START_REF] Ronnenbaum | Synergetic effects of noise from different sources: A literature study[END_REF] or with aircraft noise [START_REF] Lan | Social survey on community response to aircraft noise in Ho Chi Minh City[END_REF]). Botteldooren and Verkeyn [START_REF] Botteldooren | Fuzzy models for accumulation of reported community noise annoyance from combined sources[END_REF] studied annoyance data obtained in situ and found that this model predicted total annoyance responses well despite an overestimation of total annoyance. It was also shown to successfully predict total annoyance in laboratory conditions [START_REF] Berglund | Loudness (or annoyance) summation of combined community noises[END_REF]. Several authors (e.g. [START_REF] Miedema | Relationship between exposure to multiple noise sources and noise annoyance[END_REF][START_REF] Schulte-Fortkamp | Overall annoyance ratings in a multisource environment[END_REF]) consider the application of this model limited to situations where the specific annoyances are clearly different. Furthermore, contributions of non-dominant sources to the total annoyance are not taken into account whereas respondents may judge two noises as equally annoying (cf. [START_REF] Pierrette | Noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF][START_REF] Pierrette | Corrigendum to noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF]). Hence, nonnegligible contributions of non-dominant sources are not considered with this total annoyance model. When specific annoyances are equal or about equal, some authors argue that total annoyance should be superior to the maximum of specific annoyances [START_REF] Schulte-Fortkamp | Overall annoyance ratings in a multisource environment[END_REF], leading to the synergistic effect. Thus, the formulation of the strongest component model cannot anticipate synergistic effects or the combined noise sources paradox.

Vector summation model

The vector summation model states that total annoyance is derived from a vector summation of the specific or partial annoyances of the combined noises.

A T = a • A 1 2 + A 2 2 + 2 • A 1 A 2 cos α 1,2 0.5 + b (1.7)
where α 1,2 is a constant which depends on the combination of the different noise sources. Berglund [START_REF] Berglund | Loudness (or annoyance) summation of combined community noises[END_REF] studied pairwise combined pile driving, jack hammering, road traffic noises and an α 1,2 equal to 90°was found by iteration to give the best fit, thus setting the interaction term to zero. In another study, Berglund and Nilsson [START_REF] Berglund | Loudness of combined noises derived from singular and concurrent community noises[END_REF] predicted total loudness from pairwise combined aircraft, highway traffic and train noise using the vector summation model. They obtained strong model fits for i) α 1,2 around 90°using partial annoyances as independent variables and for ii) α 1,2 around 104°considering specific annoyances. Botteldooren and Verkeyn [START_REF] Botteldooren | Fuzzy models for accumulation of reported community noise annoyance from combined sources[END_REF] point out that values of α 1,2 greater than 90°may overcome the general overestimation observed for the strongest component model. For urban road traffic noise combined with industrial noise, Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] obtained a strong model fit using an α 1,2 equal to 101°. Ronnebaum et al. [START_REF] Ronnenbaum | Synergetic effects of noise from different sources: A literature study[END_REF] studied road traffic and railway noises and found no support of this model (no value of α 1,2 provided). Due to the lack of weights (cf. , the vector summation model cannot account for different contributions of specific annoyances [START_REF] Botteldooren | Fuzzy models for accumulation of reported community noise annoyance from combined sources[END_REF].

Linear regression model

Proposed by Berglund and Nilsson [START_REF] Berglund | Total annoyance and perceptually discernible noise sources[END_REF], the linear regression model states that total annoyance corresponds to a weighted sum of specific annoyances. This model corresponds to the perceptual form of the psychophysical independent effects model. The latter model is not presented here as it was shown not to yield satisfactory results for total annoyance prediction based on in situ annoyance data [START_REF] Pierrette | Noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF][START_REF] Pierrette | Corrigendum to noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF]. In comparison to the strongest component model the linear regression model allows to differentiate between noise sources.

A T = a 1 A 1 + a 2 A 2 + b (1.8)
If all weights would be set to one, the model would correspond to the arithmetic sum of specific annoyances. Botteldooren and Verkeyn [START_REF] Botteldooren | Fuzzy models for accumulation of reported community noise annoyance from combined sources[END_REF] tested both the strongest component and the linear regression model and concluded that the strongest component model was a better total annoyance predictor. They state that one advantage of this model over the strongest component model is that the weights may give an idea of the relative influence of each noise on total annoyance.

Mixed model

A psychophysical version of the mixed model has been recently proposed by Morel et al. [START_REF] Morel | A laboratory study on total noise annoyance due to combined industrial noises[END_REF] who studied combined road traffic and industrial noises in laboratory conditions. The model takes the following form:

A T = aA 1 + bA 2 + c|A 1 -A 2 | + d (1.9)
In addition to specific weights for the specific or partial annoyances as seen for the regression model, it introduces an interaction term which is the absolute difference between the specific or partial annoyances. The introduction of such a term is an improvement compared to the linear regression model and the strongest component model as it may account for synergistic effects. Pierrette et al. [START_REF] Pierrette | Noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF][START_REF] Pierrette | Corrigendum to noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF] also applied this model using in situ measured perceptual annoyance data. Among the models tested, the mixed model and the strongest component model yielded the best goodness-of-fit.

Summary and methodological choices

This literature review provided an overview of the fundamental observations from experiments dealing with annoyance due to single and combined noise exposure with a special focus on road traffic noise and tramway noise. It could be shown that in order to enhance noise annoyance assessment, an adequate perception-related characterization of influential acoustical features is essential. The review suggests that the annoyance potential of powered-two-wheelers requires particular attention as these noise sources are often considered as the most annoying urban road vehicle pass-by noises. Furthermore, there is a need to investigate perceptual phenomena involved in rating total annoyance due to combined urban road traffic and tramway traffic to propose models for the adequate prediction of total annoyance. This thesis comprises two main parts. The first focuses on the improvement of the physical and perceptual characterization of annoyance due to urban road vehicle pass-by noises in single noise exposure situations specifically focusing on powered-two-wheelers. Chapter 2, builds on current research and aims at improving the characterization through the application of relevant existing spectral and temporal indices. Chapter 3 focuses on the identification and characterization of annoyance-relevant perceptual features evoked by urban road vehicle pass-by noises with a specific focus on powered-two-wheelers. A semantic differential test combined with a verbalization task is carried out based on a corpus of urban road vehicle pass-by noises. The in situ recordings of urban road vehicle pass-by noises stem from Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF]. The verbalization task added to the test is associated with the item scale of annoyance; its aim is to gain additional descriptions of potential factors influencing noise annoyance. Based on the ratings of the semantic differential test and the descriptions obtained in the verbalization task, relevant perceptual features can be identified and adapted indices proposed. In Chapter 4, the proposed indices are tested using short-term annoyance due to urban road vehicle pass-by noises assessed in two experiments carried out in an imaginary context in laboratory conditions. This step allows for the proposal of an acoustical indicator of noise annoyance due to different urban road vehicle pass-by noises.

The focal points of the second part is on the prediction of total annoyance due to urban road traffic noise combined with tramway noise. i) on the understanding of perceptual phenomena involved in total annoyance due to urban road traffic noise combined with tramway noise, and ii) on the characterization of total annoyance due to combined urban road traffic and tramway noises.

In a first step, perceptual phenomena involved in total annoyance due to combined road traffic and tramway noise are investigated and compared to findings from literature (cf. Chapter 5). Three combined noise experiments using different experimental methodologies and different apparatus are analyzed allowing for a comparison of the observed phenomena across the three experiments. The first experiment (Exp. A) is carried out in an imaginary context in laboratory conditions. Noise sequences are constructed based on the urban road vehicle pass-by noises considered in Chapters 2-4 and on tramway noises recorded in situ and characterized by Trollé et al. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF][START_REF] Trollé | Corrigendum to short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF]. Both road traffic and tramway traffic sequences are created comprising different traffic compositions and simulating peak and off-peak hour traffic. The noise sequences are combined respecting the recommendations given by Berglund [START_REF] Berglund | Empirical issues concerning annoyance models for combined community noises[END_REF]. Thus, in the first experiment all the combinations of five road traffic sequences and five tramway traffic sequences are studied. The second experiment carried out in an experimental living room allows to study annoyance in a more realistic environment. For this experiment, the road traffic and tramway traffic sequences of the first experiment are used with increased duration. The third experiment (Exp. C) was conducted by IFSTTAR Bron in the framework of the project VIA URBA [START_REF] Philipps-Bertin | Etude qualitative de la gêne en situation de multi-exposition au bruit du tramway et au bruit routier urbain[END_REF][START_REF] Champelovier | Perception du bruit du tramway dans des situations contrastées de bruit de trafic routier urbain[END_REF]. It allows to investigate perceptual phenomena present in different situations of combined urban road traffic and tramway noises in a simulated environment.

The second step is concerned with the prediction of total annoyance using relevant perceptual and psychophysical models total annoyance. These models are selected based on the observations made in Chapter 5 regarding the perceptual phenomena involved in total annoyance due to the combination of road traffic and tramway traffic noises. Furthermore, it benefits from the physical and perceptual characterization carried out for urban road vehicle pass-by noises (cf. Chapters 2-4) and for the tramway noise (cf. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF][START_REF] Trollé | Corrigendum to short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF]). The psychophysical and perceptual total annoyance models are constructed using the annoyance data obtained in the laboratory experiment (first experiment). Their prediction quality is assessed using the data obtained in the simulated environment experiment in order to propose a total annoyance models relevant for urban road traffic noise combined with tramway noise.

Chapter 2

Annoyance characterization using existing spectral and temporal indices

In this chapter, different spectral and temporal indices from existing literature will be calculated for urban road vehicle pass-by noises recorded by Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] and Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF][START_REF] Morel | Physical and perceptual characterization of road traffic noises in urban areas for a better noise annoyance assessment[END_REF]. The aim of this work is to improve the characterization of noise annoyance by using indices which have been successfully applied in recent literature dealing with transportation noise annoyance (e.g. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF]).

Introduction

The acoustical structure of urban road traffic noise is complex. Pass-by noises, such as noises of heavy vehicles, light vehicles, buses and powered-two-wheelers, in acceleration, at constant speed or in deceleration, are physically and perceptually different due to their specific spectral and temporal signal features [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF][START_REF] Morel | Physical and perceptual characterization of road traffic noises in urban areas for a better noise annoyance assessment[END_REF].

The current study is closely linked to previous research conducted by Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF][START_REF] Morel | Physical and perceptual characterization of road traffic noises in urban areas for a better noise annoyance assessment[END_REF]. They performed a listening experiment in laboratory conditions consisting of a free categorization task followed by a free verbalization of 57 urban road singlevehicle pass-by noises recorded in situ. The aim of the categorization task was to form groups of pass-by noises according to their perceived similarities. Based on statistical and linguistic analyses, a perceptual and cognitive typology was suggested mainly structured by reference to the vehicle type and the vehicle driving condition. Seven perceptual categories were proposed consisting of powered-two-wheelers 1 at constant speed (Category 1), powered-two-wheelers in acceleration (Category 2), buses, heavy and light vehicles at constant speed (Category 3), powered-two-wheelers in deceleration (Category 4), buses, heavy and light vehicles in deceleration (Category 5), light vehicles in acceleration (Category 6) and buses and heavy vehicles in acceleration (Category 7) [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF]. For each group they created, the participants were asked to identify one pass-by noise they considered most representative. The auditory spectrograms of these pass-by noises are presented in Appendix A.1.

Then, short-term noise annoyance due to the various pass-by noises was assessed per perceptual category in further listening experiments. A verbalization task associated with the annoyance experiments suggested the impact of spectral features on noise annoyance responses. To characterize the spectral features, Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] and Morel et al. [START_REF] Morel | Physical and perceptual characterization of road traffic noises in urban areas for a better noise annoyance assessment[END_REF] proposed different acoustic and psychoacoustic indices. Spectral energy in different frequency ranges was assessed using the indices L LF , L MF , L HF calculated from the third octave spectrum between 25 and 250 Hz, 315 and 1250 Hz, 1.6 and 12.5 kHz, respectively (cf. [START_REF] Alayrac | Annoyance from industrial noise: indicators for a wide range of industrial sources[END_REF]). Trollé et al. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF] studied noise annoyance due to tramway noise and highlighted the relevance of spectral features. Free verbalizations collected after the experiment revealed that the bass/treble character of the tramway noises likely influenced short-term annoyance. The TETC (Total Energy of Tonal Components) index was proposed taking into account the energy of tonal components in high frequencies. This index was found to be better adapted than the L HF index representing the A-weighted sound pressure level in high frequencies [START_REF] Trollé | Indicateurs acoustiques de la gêne en situation de mono-exposition au bruit de tramway[END_REF]. Thus, the TETC index could be useful enhancing the characterization of noise annoyance due to urban road traffic noise.

Based on the verbalization task data, Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF][START_REF] Morel | Physical and perceptual characterization of road traffic noises in urban areas for a better noise annoyance assessment[END_REF] highlighted two important temporal characteristics influencing noise annoyance responses: on the one hand, global amplitude variations (in the following labeled "irregular amplitude variations") of the pass-by noises and on the other, periodic amplitude variations played an important role. The free categorization task revealed that the irregular amplitude variations may be linked to the different driving conditions of the vehicles (acceleration, deceleration, constant speed). Periodic amplitude variations were attributed to the powertrain of the vehicles which are predominant in urban areas. To characterize these features five different indices were used: roughness R, the sound pressure level (SPL) growth rate in time ∆L + , the SPL decrease rate in time ∆L -, the growth rate of loudness in time ∆N + and the decrease rate of loudness in time ∆N -. For the first category, composed of powered-two-wheelers passing by at constant speed, only loudness was included in the final regression model. However, for this category of pass-by noises, the analysis of the free verbalization task showed that terms referring to specific temporal features, such as "snoring", were used to characterize the noises. This indicates that pass-by noises elicit specific sensations related to the temporal structure which also might have an impact on annoyance. Furthermore, powered-two-wheelers especially were associated with negative assessment. Common psychoacoustic indices, such as roughness, were not found to be capable of sufficiently accounting for these sensations evoked by the powered-two-wheeler pass-by noises.

The aim of the current research is to improve the physical and perceptual characterization of urban road vehicle pass-by noises in terms of noise annoyance. Spectral and temporal indices found in recent literature (e.g. TETC in [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF]) will be calculated for the pass-by noises belonging to the seven perceptual categories. Furthermore, two different roughness models will be tested. Then, correlation analyses will be carried out between the values of the indices and the mean annoyance responses per category obtained by Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF].

Total energy of tonal components (TETC )

First, a spectral index, derived from tonal components as a function of the criticalband rate "frequency tracks" calculated using the software dBSonic© 01dB-Metravib, will be tested. Frequency tracks represent audible tonal components of a sound and are based on frequency contours and the Gestalt theory by Terhardt [START_REF] Terhardt | From speech to language: On auditory information processing[END_REF]. The Gestalt theory is a concept of perceptual organization which describes human information processing as being arranged in hierarchical steps (cf. [START_REF] Trollé | Évaluation auditive de sons rayonnés par une plaque vibrante a l'intérieur d'une cavité amortie : ajustement des efforts de calcul vibroacoustique[END_REF]). The Gestalt of a certain entity is composed of single parts but recognized as a whole.

Frequency tracks are formed on the basis of various parameters that identify components as "noisy" or "tonal". When a component is identified as tonal it is added to a track and either connected to a neighbouring element (forming the track) or detected as a single impulsive event. Components not belonging to tracks are rejected as noise. The underlying criteria are as follows [1]:

• Frequency search range: 0.15 Barks. Determines up to which frequency spacing neighboring contour points are linked together. • Level search range: 10 dB. Contour points, falling into the frequency search range and differing in level more than 10 dB, are not connected.

The TETC index is calculated from summing the maximal energy of tonal components in low-, mid-and high-bark ranges. The definition for the Bark ranges was derived from the low-, mid-, and high frequency ranges used in literature. The bark bands are shown in Table 2.1. The TETC index calculated from 12 to 24 Barks has been proposed to account for the high-frequency content due to the squeal noise of single tramway pass-bys [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF]. In this analysis, the index will be calculated in different frequency bands (cf. Table 2.1) for the studied urban road vehicle pass-by noises. It is defined as follows

T ET C = 10 log 10 y-0.03 x-0.98 10 L(z) 10 dz dB (2.1)
where x2 is the lower limit (in Barks) of the frequency band and y is the upper limit (in Barks) of the frequency band under investigation. L(z) represents the maximal level (across time) of the tonal components as a function of the critical-band rate z. The maximal tonal component level as a function of the critical-band rate is calculated from the auditory spectrogram (e.g. [START_REF] Heinbach | Aurally adequate signal representation: The part-tone-timepatter[END_REF][START_REF] Terhardt | From speech to language: On auditory information processing[END_REF]). The TETC index is calculated based on this representation by summing the maximal energy of the tonal components within different critical bands. Maximal values across left and right channels are used 3 .

The results obtained from Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] and the results of the correlation analysis using the TETC index are summarized in Table 2.2. To characterize spectral energy in different frequency bands, Morel determined the indices L LF , L MF and L HF representing the mean (across time) A-weighted sound pressure levels in low, mid and high frequencies obtained from the third-octave-band spectrum [START_REF] Alayrac | Annoyance from industrial noise: indicators for a wide range of industrial sources[END_REF].

Table 2.2:

Comparison of correlation coefficients obtained from the characterization of annoyance using the spectral indices L LF , L MF , L HF calculated from the third octave spectrum between 25 and 250 Hz, 315 and 1250 Hz, 1.6 and 12.5 kHz (cf. Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF]) and the values of the TETC index calculated in low-(1-2 Barks), mid-(3-10 Barks) and high-(11-24 Barks) frequency bands. The strongest correlation coefficients for each category are highlighted in bold characters. a p < 0.001, b p < 0.01, c p < 0.05. The results obtained from the TETC analysis slightly vary in comparison with Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] but the global tendency remains consistent and both indices correlate strongly with mean annoyance per perceptual category. In general, the characterization of annoyance per perceptual category using the TETC index yields no remarkable improvement in comparison to the acoustical indices L LF , L MF and L HF used by Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF].

Cat. L LF L MF L HF T ET C LF T ET C MF T ET C HF

Characterization of irregular amplitude variations

The importance of irregular amplitude variations influencing noise annoyance judgments was shown by Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF]. To characterize these temporal features, the SPL growth rate in time ∆L + (dB(A)/s), the SPL decrease rate in time ∆L -(dB(A)/s), the growth rate of loudness in time ∆N + (sone/s) and the decrease rate of loudness in time ∆N -(sone/s) were proposed. In order to improve the characterization of irregular amplitude variations, four additional indices are tested based on the A-weighted pressure varying over time. The V AP norm index (Variance of timevarying A-weighted Pressure normalized by the root-mean-square (RMS) A-weighted pressure) was found to adequately characterize amplitude variations caused by tramway pass-bys with regard to annoyance. In this analysis, the index will be applied to annoyance data of urban road vehicle pass-by noises (Eq. 2.2). In order to test possible improvements, the V AP norm index was also tested without normalization (Eq. 2.3).

V AP norm = V (p A (t)) p RMS A (2.2) V AP = V (p A (t)) (2.3)
where V represents the finite-sample variance operator, p A (t) the time-varying A-weighted pressure and p RMS A represents the RMS A-weighted pressure, defined as follows:

p RMS A = 1 T T 0 p 2 A (t)dt (2.4)
Furthermore, the ST DP norm index was calculated which represents the standard deviation of the time varying A-weighted pressure normalized by RMS A-weighted pressure. This index was also tested without normalization (STDP ). The indices are defined as follows:

ST DP norm = SD(p A (t)) p RMS A (2.5) ST DP = SD(p A (t)) (2.6)
where SD is the finite-sample standard deviation operator, p A (t) the time-varying A-weighted pressure and p RMS A the RMS A-weighted pressure (see Eq. 2.4). For comparison, the correlation coefficients obtained using the indices ∆L + , ∆L -, ∆N + and ∆N -are also presented.

The correlation coefficients calculated between these indices and the mean annoyance responses per category are shown in Table 2.3. The highest correlation coefficients are obtained using the index STDP. Interestingly, the STDP index correlates only moderately with mean annoyance responses due to the poweredtwo-wheelers in acceleration (Category 2) and in deceleration (Category 4). Using this index, moderate to strong correlation coefficients are obtained for all categories compared to the indices proposed by Morel [75]. This index will be considered in the following to account for irregular amplitude variations of urban road vehicle pass-by noises.

Characterization of pass-by noises using different roughness models

As indicated before, Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] highlighted the importance of temporal features, such as periodic amplitude variations, with regard to noise annoyance due to urban road vehicle pass-by noises. In the following analysis, two different roughness models will be used to test for improved characterization of mean annoyance responses. Both models are compared with regard to the characterization of annoyance. Then, the methodology and the results of a pilot experiment carried out in order to test the roughness models will be described.

Oldenburg roughness model

Recent studies by Yasui and Miura [START_REF] Yasui | Perception of roughness on sounds amplitude modulated with triangular wave[END_REF] and Weber et al. [START_REF] Weber | Shape of the envelope and roughness perception[END_REF] revealed that the shape of the envelope of amplitude modulated sinusoids has an impact on the roughness sensation. Both research groups conducted experiments where they varied the steepness of the ascending and the descending slopes of amplitude modulated sinusoids with triangularly shaped envelopes. It was found that unsymmetrical shapes of the envelopes result in higher roughness than symmetrical ones. The sound with the steepest ascending slope was perceived as being the most "rough".

Current roughness models are based on evaluations of constant sinusoidally amplitude modulated signals [START_REF] Aures | Ein Berechnungsverfahren der Rauhigkeit (A procedure for calculating auditory roughness)[END_REF]. However, environmental noise is usually not composed of such sinusoidal modulation. A new approach developed by Oetjen et al. [START_REF] Oetjen | Rauigkeitsberechnung unter Berücksichtigung der Einhüllendenform[END_REF] takes into account the shape of the modulation envelope in each auditory channel in the roughness calculation.

Presentation of the model

The different stages of roughness calculations can be separated into two main parts. This description is based on the presentation of the published model [START_REF] Oetjen | Rauigkeitsberechnung unter Berücksichtigung der Einhüllendenform[END_REF]. The first part works as an "auditory frontend" and derives the envelope through demodulation and the second calculates roughness based on the envelope spectrum. This first unit is composed of a Gamma tone filter bank, half-wave rectification, low-pass filtering in order to remove the fine structure of the waveform, envelope processing based on the venelope and a compressor. The venelope is defined as the second-order envelope [START_REF] Ewert | Spectro-temporal processing in the envelopefrequency domain[END_REF]. The venelope spectrum contains equally spaced components with a fundamental at the beat frequency and harmonics that decrease in amplitude with increasing frequency [START_REF] Ewert | Spectro-temporal processing in the envelopefrequency domain[END_REF].

Based on auditory evaluations of amplitude modulated sinusoids [START_REF] Fastl | Psychoacoustics -Facts and Models[END_REF], it was found that roughness is dependent on the center frequency as well as the modulation frequency. The dependency of roughness on the center frequency is implemented by applying a channel dependent weighting factor. The dependency of the modulation frequency has a band pass characteristic: At a center frequency of 1 kHz maximum roughness appears near modulation frequencies of 70 Hz [START_REF] Fastl | Psychoacoustics -Facts and Models[END_REF]. When considering non-sinusoidal modulations, the dependency of the modulation frequency can be reproduced by determining the fundamental frequency in the modulation spectrum [START_REF] Oetjen | Rauigkeitsberechnung unter Berücksichtigung der Einhüllendenform[END_REF]. This fundamental frequency is extracted using a band pass filter. Yasui and Miura [START_REF] Yasui | Perception of roughness on sounds amplitude modulated with triangular wave[END_REF] found that the roughness sensation is influenced by the harmonics of this fundamental frequency. The new approach searches for multiples of the most dominant frequency in the unfiltered modulation spectrum. These multiples are then added to the band pass filtered spectrum.

The harmonics of the fundamental frequency of the modulated spectrum are extracted by subtracting the filtered spectrum from the unfiltered spectrum. The components relevant for the roughness sensation are the sum of the fundamental frequency and the extracted harmonics. Furthermore, it is assumed that "rough" components of the modulation spectrum are being masked by components that are not "rough". The masking model determines the RMS of the components that are not relevant for roughness processing and adds the exponent alpha to the calculation. The components which are relevant for roughness processing are then weighted with this factor. Fastl and Zwicker [START_REF] Fastl | Psychoacoustics -Facts and Models[END_REF] found that modulated broadband sounds elicit a higher roughness sensation than modulated narrow band signals. This dependency is realized through computing a cross correlation with the signal from the adjacent channel. The sum of the RMS roughnesses per channel forms the global roughness of a sound.

Application to annoyance data

For further analysis, indices determined using the Oldenburg roughness model [START_REF] Oetjen | Rauigkeitsberechnung unter Berücksichtigung der Einhüllendenform[END_REF] were correlated with annoyance responses obtained by Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF]. In the following roughness indices are calculated using maxima (R max ), mean (R mean ) and percentiles (R 5 , R 10 , R 50 , R 90 , R 95 ). Table 2.4 shows the correlation coefficients per category for the seven roughness indices. Roughness indices determined using the Oldenburg roughness model [START_REF] Oetjen | Rauigkeitsberechnung unter Berücksichtigung der Einhüllendenform[END_REF] correlate well with the annoyance responses. The highest correlation coefficients are obtained using the index R 90 .

dBSonic roughness model

For the purpose of comparison with the results of Section 2.4.1.2, it was considered important to test the roughness model implemented in the software dBSonic© 01dB-Metravib by calculating the same roughness indices (as seen in Table 2.4) and by correlating their values with the annoyance responses collected by Morel [75]. This section will thus present Morel's [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] results obtained with the roughness indices calculated using dBSonic such as R mean .

Presentation of the model

The software description states that the implemented model for the calculation of roughness is based on Aures model (see [START_REF] Aures | Ein Berechnungsverfahren der Rauhigkeit (A procedure for calculating auditory roughness)[END_REF] for further details on the model). It states that the model used is an "improved" model but no details are given on the modifications with regard to the roughness model of Aures. Thus, in the following this model will be referred to as the "dBSonic roughness model".

Application to annoyance data

To calculate roughness using dBSonic, the roughness sequence length (RSL) must be defined. The RSL defines the length of the loudness data windows used to compute specific roughness and overall roughness for a point in time [1]. In the software description it is mentioned that most reliable results may be obtained using an RSL of 500 ms for stimuli of 4 s in duration. When sounds with a shorter duration are analyzed, an RSL of 200 ms is supposed to be more accurate. The default RSL parameter is 200 ms. As the road vehicle pass-by noises used in this study vary in their duration between 3 s to 9 s, it would be interesting to study the effects of the two different RSL parameters. Roughness over time is calculated for one powered-two-wheeler passing by at constant speed (Category 1) which is 4 s in duration. Using different RSLs seems to have a minor influence on roughness calculation. In addition, the difference between mean roughness values calculated using both RSL parameters for this pass-by noise is only small (R mean RSL=500ms = 13.3 cA; R mean RSL=200ms = 11.9 cA).

In the following, the seven roughness indices are calculated using the RSL of 200 ms. Table 2.5 shows the correlation coefficients between mean annoyance responses and the seven roughness indices. As observed for the Oldenburg roughness model (cf. Table 2.4), the highest correlation coefficients are obtained using the index R 90 but also R 95 . The indices calculated with dBSonic seem slightly higher than the ones calculated with the Oldenburg roughness model. A dedicated comparison between the two models is carried out in the next section.

Testing of roughness models with measured roughness

The Oldenburg and the dBSonic roughness models will be tested using roughness ratings obtained in a small-scale pilot experiment carried out at the University of Oldenburg. The following sections describe the methodology as well as the comparison of both roughness models based on measured roughness.

Pilot experiment

In order to evaluate the performance of the roughness models, a pilot experiment was conducted. In this experiment, participants were asked to rate the roughness character of 33 urban road vehicle pass-by noises.

Stimuli Out of each of the seven perceptual categories [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF], a maximum of five stimuli were selected. In total, 33 stimuli equalized in A-weighted sound pressure level were used and played back in random order to avoid bias due to scaling order effects. The pass-by noises were presented at an average sound pressure level of 60 dB(A). The duration of the pass-by noises ranged between 3 and 9 s.

Apparatus

The experiment was carried out in a quiet room. It was controlled using a laptop and the software Windows Mediaplayer©. The sound reproduction system consisted of two stereo loudspeakers which were placed on a table 2 m in front of the participants. Thus, the interaural axis at the center position and the loudspeakers formed an equilateral triangle.

Participants The experiment was performed by 12 male and 3 female partici- pants. They were aged between 21 and 65 years and were familiar with acoustic terminology. Participants were either students or researchers of the Acoustics Group at the University of Oldenburg. All participants declared normal hearing abilities.

Procedure Prior to the start of the experiment, the participants listened to the total stimuli corpus to become familiar with the pass-by noises. Then, the participants were asked to rate the roughness characteristic of each urban road vehicle pass-by noise on a categorical scale ranging from 0 ("not rough") to 7 ("very rough"). The stimuli were played back one by one and in random order. Instructions were given verbally. In each run a maximum of five participants took part simultaneously.

Results

In the following, the roughness ratings (measured roughness) will be compared to the values of the roughness indices calculated using the Oldenburg roughness model [START_REF] Oetjen | Rauigkeitsberechnung unter Berücksichtigung der Einhüllendenform[END_REF] and the dBSonic roughness model [1]. As the roughness ratings were measured on an ordinal scale, median values were used as a measure of central tendency. Spearman correlation coefficients (r s ) were determined to assess the relationship between measured roughness and the values obtained using the roughness models.

Oldenburg roughness compared to measured roughness In order to assess the relationship between the indices calculated using the Oldenburg roughness model and the median values of the measured roughness, Spearman's rank correlation coefficients were determined. Table 2.6 shows the correlation coefficients between the median values for the measured roughness and the values of seven indices calculated using the Oldenburg roughness model.

Table 2.6: Spearman's rank correlation coefficients ( a p < 0.001, b p < 0.01, c p < 0.05) between the median values for the measured roughness and the values of seven indices calculated using the Oldenburg roughness model.

R max R mean R 5 R 10 R 50 R 90 R 95
Measured roughness -0.13 0.06 -0.03 -0.01 0.01 0.1 0.06 (median)

As can be seen in Table 2.6, the correlation analysis yields no significant correlation coefficients. In order to analyze the characterization of the Oldenburg roughness model further, the relationship between the median values for the measured roughness and the values of the index R 90 for the seven categories is illustrated in Figure 2.2. The pass-by noises are labelled according to the code xyz_N as used in [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF]: x for "vehicle type" (b = bus; d = powered-two-wheeler; p = heavy vehicle; v = light vehicle), y for "driving condition" (a = acceleration; d = deceleration; f = constant speed), z for "road morphology" (o = open street; u = U-shaped street). N is an arbitrary number to differentiate stimuli.

From Figure 2.2 it can be observed that the data points are grouped together. According to the model, one light vehicle at constant speed (vfu_16) is characterized to be as rough as a powered-two-wheeler in acceleration (dao_3). Based on evaluations by Oetjen et al. [START_REF] Oetjen | Rauigkeitsberechnung unter Berücksichtigung der Einhüllendenform[END_REF] of their roughness model, it was observed that the model has difficulties in calculating the roughness of vehicle pass-by noises at constant speed (60 km/h). For these noises, the calculated roughness was overestimated. These issues seemed to have appeared in this study as well. Especially for buses, light vehicles and heavy vehicles passing by at constant speed (Category 3), the calculated roughness values do not correspond to the measured roughness values.

Furthermore, the pass-by noise dao_2 is calculated rougher than the pass-by noise dau_5. However, the measured roughness values of both pass-by noises are not in agreement with the calculated roughness values. The calculated roughness To further analyze this outcome, the modulation spectra of the two pass-by noises dao_2 and dau_5 belonging to Category 2 are presented in Figure 2.3. From these graphs it can be seen that amplitude modulations are inherent in both pass-by noises. The peak at 0 Hz corresponds to the dc-value and the peaks in the spectrum correspond to the most prominent modulation frequencies. The pass-by noise dao_2 ( comprises amplitude modulations between approximately 13 and 50 Hz. In literature, these modulation frequencies are covered by the indices roughness and fluctuation strength describing different sensations due to envelope fluctuations [START_REF] Fastl | Psychoacoustics -Facts and Models[END_REF]. According to Fastl and Zwicker [START_REF] Fastl | Psychoacoustics -Facts and Models[END_REF], fluctuation strength accounts only for modulations up to 20 Hz with a maximum around 4 Hz. In contrast, roughness appears in the range of modulation frequencies between 15 Hz and 300 Hz with a maximum at 70 Hz [START_REF] Fastl | Psychoacoustics -Facts and Models[END_REF]. Hence, it seems that the transition between both roughness and fluctuation strength indices is continuous and difficult to objectively determine. The pass-by noise dau_5, for example, (as illustrated in Figure 2.3(b)) consists of amplitude modulations in the range of approximately 13 Hz to 50 Hz with maxima at 17 and 34 Hz. Consequently this pass-by noise may be composed of amplitude modulations below and above the "limit" and both fluctuation strength and roughness could be present. When participants were asked to rate the roughness character they might have only focused on the general perception of amplitude modulations and were not able to distinguish between the theoretical definitions of roughness and fluctuation strength. This task appeared to be very complicated due to the complexity of the pass-by noises.

Hence, the pass-by noise dao_2 was characterized rougher than the pass-by noise dau_5 by the Oldenburg roughness model which, based on the modulation analysis, appears meaningful. dBSonic roughness compared to measured roughness The correlation coefficients determined between the median values for the measured roughness and the values of the roughness indices calculated with the roughness model implemented in dBSonic are presented in Table 2.7.

Table 2.7: Spearman's rank correlation coefficients ( a p < 0.001, b p < 0.01, c p < 0.05) between the median values for the measured roughness and the values of seven indices calculated using the dBSonic roughness model.

R max R mean R 5 R 10 R 50 R 90 R 95
Measured roughness 0.51 b 0.59 a 0.50 b 0.43 b 0.46 c 0.05 0.08 (median)

The highest correlation coefficient is obtained using the roughness index R mean . To further study the relationship between the index values and the median values for the measured roughness, Figure 2.4 shows a scatter plot. Focusing on the two previously analyzed pass-by noises it can be observed that the pass-by noise dau_5 (median value for measured roughness = 7; R mean = 38.1 cAsper) was characterized as being rougher than the pass-by noise dao_2 (median value for measured roughness = 4; R mean = 26.6 cAsper). This is not in accordance with the observations made regarding the modulation spectra (cf. Figure 2.3). This may be due to the fact that this roughness model was developed based on sinusoidally amplitude modulated signals. The urban road vehicle pass-by noises under investigation exhibit different and more complex shaped envelopes which may have lead to these results.

Conclusion

The aim of this investigation was to improve the characterization of annoyance due to urban road vehicle pass-by noises. Spectral and temporal indices and a roughness model developed in recent literature (e.g. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF], [START_REF] Oetjen | Rauigkeitsberechnung unter Berücksichtigung der Einhüllendenform[END_REF]) were tested and applied to the annoyance responses collected by Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF]. Furthermore, a preliminary study was carried out to compare two roughness models by using roughness responses.

The TETC index derived from the energy in tonal components correlated well with the annoyance responses per perceptual category. Compared to the acoustical indices L LF , L MF , L HF applied by Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] no improvements in terms of the correlation coefficients could be observed.

Concerning the characterization of temporal features and more specifically irregular amplitude variations, four indices based on the A-weighted sound pressure level were tested. A correlation analysis between the mean annoyance responses per category [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] and index values revealed strong positive correlations using the index STDP (Standard Deviation of the time-varying A-weighted sound pressure). In comparison to ∆L + , ∆L -, ∆N + and ∆N -(cf. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF]), higher correlation coefficients were obtained for each category. However, only moderate correlation coefficients were obtained for powered-two-wheelers in acceleration (Category 2) and in deceleration (Category 4). Furthermore, two different roughness models were tested: the Oldenburg roughness model [START_REF] Oetjen | Rauigkeitsberechnung unter Berücksichtigung der Einhüllendenform[END_REF] and the dBSonic roughness model [1]. The roughness indices calculated using both models correlated well with mean annoyance responses per category. The magnitude of the correlation coefficients obtained using the dBSonic roughness model were generally superior to the Oldenburg roughness model. However, pass-by noises belonging to Category 2 and Category 4 correlated only moderately with the roughness indices obtained with the dBSonic roughness model. In a pilot study, participants were asked to rate the roughness character of 33 urban road vehicle pass-by noises stemming from the typology proposed by Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF]. Correlation analysis showed that the measured roughness was better correlated with the values of the R mean index obtained using the dBSonic roughness model. A modulation analysis indicated that two powered-two-wheelers in acceleration (dao_2 and dau_5) may elicit different modulation-related sensations. Taking into account the shape of the envelope of the signals in the roughness calculation, the Oldenburg roughness model may be more appropriate to characterize these two powered-two-wheelers. However, the results showed that it must be refined which was also concluded by Oetjen et al. [START_REF] Oetjen | Rauigkeitsberechnung unter Berücksichtigung der Einhüllendenform[END_REF]. This investigation showed that there is a need for an improved and meaningful characterization of annoyance due to urban road vehicle pass-by noises and especially due to powered-two-wheelers. In the following chapter, auditory attributes influencing noise annoyance due to urban road vehicle pass-by noises will be studied in more detail. In this chapter, the pass-by noises have been tested per category rather than mixed together, thus producing little variation between stimuli per category. The following chapters will study the urban road vehicle pass-by noises of different vehicle types and driving conditions together to allow the analysis of influential auditory attributes and to improve the characterization of annoyance.

Introduction

The models that relate noise annoyance assessments to noise signal parameters are mainly based on energy-related indices (e.g. L den ). However, various studies have shown that sound characteristics of environmental noises linked to temporal and spectral signal features also influence noise annoyance responses. In order to enhance noise annoyance models, an adequate perception-related characterization of these influential acoustical features is essential. On the basis of urban road single-vehicle pass-by noises, this study will show the identification of spectral and temporal signal properties that are related to noise annoyance. For this purpose, a semantic differential test with a verbalization task is carried out on urban road vehicle pass-by noises stemming from the different perceptual categories of the typology proposed by Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF]. In Section 3.2, the experimental methodology is presented. Section 3.3 highlights the main perceptual features that emerged from the analysis, and the characterization of different sensations evoked by urban road single-vehicle pass-by noises. Section 3.4 proposes two indices characterizing modulation-related sensations.

Semantic differential test with verbalization task

The main perceptual features evoked by urban road single-vehicle pass-by noises are investigated using a semantic differential test and a verbalization task, in which participants are asked to explain their annoyance rating for each stimulus.

Stimuli

In order to get a maximal variety in vehicle types and driving conditions of urban road single-vehicle pass-by noises, two stimuli per perceptual category of the typology proposed by Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] and Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF][START_REF] Morel | Physical and perceptual characterization of road traffic noises in urban areas for a better noise annoyance assessment[END_REF] were selected: the prototype for each category was chosen and another stimulus per category was selected with regard to its difference in annoyance rating. The final corpus of stimuli consisted of 14 single pass-by noises. Table 3.1 presents the single pass-by noises employed in the experiment. The auditory spectrograms of these pass-by noises are shown in Appendix A.1 and A.2. The stimuli were recorded by Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF] in Lyon (France) and its suburbs using the ORTF technique (devised at the former French Broadcasting Corporation -Two Schoeps MSTC 64 microphones with Schoeps BBG windfields) in accordance with French standards NF S31-010 [START_REF]Acoustique -Caractérisation et mesurage des bruits de l'environnement[END_REF] and NF S31-085 [START_REF]Caractérisation et mesurage du bruit dû au trafic routier[END_REF]. Several researchers highlighted the advantages of this recording technique (e.g. [START_REF] Lavandier | The contribution of sound source characteristics in the assessment of urban soundscapes[END_REF]) for stereophonic sound reproduction in laboratory as it is known for its good representation, readability, plausibility and overall reproduction quality of fixed and moving noise sources [START_REF] Guastavino | Spatial audio quality evaluation: comparing transaural, ambisonics and stereo[END_REF]. To reduce the effects due to loudness variation, the stimuli were equalized to an A-weighted equivalent sound pressure level of 60 dB(A). This sound pressure level reproduced in laboratory was measured at the ear position of the participants using an artificial head (Cortex Manikin MK2/NCF1). No filter simulating façcade transmission was applied to the stimuli as wall material and window types have an effect on auditory judgements [START_REF] Trollé | An analysis of the effects of structural parameter variations on the auditory perception of environmental noises transmitted through a simulated window[END_REF] and the choice of one kind of façade might have been too limiting. Thus, the worst noise exposure is considered (e.g. [START_REF] Alayrac | Annoyance from industrial noise: indicators for a wide range of industrial sources[END_REF]), such as being in private outdoor spaces with a certain distance from the road.

The length of the stimuli was imposed by the original duration of the single pass-by noises recorded in situ [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF], and varied for the 14 studied stimuli between 3 s and 7 s. Previous studies demonstrated that the duration of stimuli has a limited or no influence on short-term noise annoyance. Paulsen [START_REF] Paulsen | On the influence of the stimulus duration on psychophysical judgment of environmental noises taken in the laboratory[END_REF] showed that 1 s-and 20 s-durations of highway road traffic noises had a small influence on annoyance judgments compared to longer durations of 50 s and 80 s. Parry and Parry [START_REF] Parry | The interpretation and meaning of laboratory determinations of the effect of duration on the judged acceptability of noise[END_REF] concluded that duration was not an inherent factor in the noisiness judgments of aircraft flyover noises. For urban road single-vehicle pass-by noises, Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] and Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF][START_REF] Morel | Physical and perceptual characterization of road traffic noises in urban areas for a better noise annoyance assessment[END_REF] found that stimulus duration, ranging from 3 to 9 s, was not a criterion to formulate annoyance judgments. The same conclusion was drawn by Trollé et al. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF] for single tramway pass-by noises with durations ranging from 8 to 25.5 s.

Apparatus

The experiment took place in a quiet room with a background noise measured at 19 dB(A). The stimuli were reproduced employing a 2.1 audio reproduction system consisting of two active loudspeakers (Dynaudio Acoustics BM5A) and one active subwoofer (Dynaudio Acoustics BM9S).

Concerning the positioning of listener and loudspeaker, the center of the interaural axis of the listener and the loudspeakers formed an equilateral triangle. This was in accordance with the recommendations given by Bech and Zacharov [START_REF] Bech | Perceptual audio evaluation. Theory, method and application[END_REF]. The loudspeakers were placed at a height of 1.20 m from the floor, and the subwoofer was placed on the floor between the loudspeakers. The user interface was programmed using MATLAB©.

Participants

The experiment was performed by 14 participants (3 female, 11 male) aged between 23 and 64 years (mean age = 35 years; standard deviation = 12 years). The panel consisted of acoustic experts who were PhD students and doctors in acoustics. All participants declared normal hearing abilities. The participants were paid for their participation.

Items of the semantic differential

The set of items for scales of the semantic differential test was primarily constructed based on data of the free verbalization test carried out by Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF] with the same type of noises. The descriptive terms used for the semantic differential are presented in Table 3.2 in which they are displayed according to four main groups referring to spectral, temporal and intensity aspects as well as hedonic judgments of the noises. Appropriate adjectives were combined, usually resulting in bipolar scales (e.g. "variable/constant"). If appropriate antonyms did not exist in the original French language, the items were used for unipolar scales with the labels "not at all" ("pas du tout") and "extremely" ("extrêmement") as opposing scale descriptors as recommended by Guski [START_REF] Guski | Psychological methods for evaluating sound quality and assessing acoustic information[END_REF]. The rating scales consisted of seven categories with numerical labels ranging from 1 to 7 2 .

The following items found in literature were also included: "slow/fast" [START_REF] Altinsoy | The semantic space of vehicle sounds: Developing a semantic differential with regard to customer perception[END_REF], "dynamic" [START_REF] Raimbault | Simulation des ambiances urbaines: Intégration des aspects qualitatifs[END_REF] and "bright" [START_REF] Kuwano | Subjective evaluation of car door sound[END_REF]. The unipolar item "tonal" was added to the set, too. The final item set was composed of 11 bipolar and 14 unipolar scales:

• Bipolar scales: "smooth/rough" ("lisse/rugueux"), "deep/sharp" ("grave/aigu"), "hard/soft" ("dur/doux"), "loud/soft" ("fort/faible"), "shrill/dull" ("strident/ sourd"), "slow/fast" ("lent/rapide"), "variable/constant" ("variable/constant"), "pleasant/unpleasant" ("agréable/désagréable"), "ascending/descending" ("croissant/décroissant"), "progressive/sudden" ("progressif/soudain"), "intermittent/continuous" ("discontinu/continu");

• Unipolar scales: "disturbing" ("dérangeant"), "bright" ("brillant"), "staccato" ("saccadé"), "aggressive" ("agressif"), "nasal" ("nasal"), "hissy" ("sifflant"), "tonal" ("tonal"), "stressful" ("stressant"), "metallic" ("métallique"), "humming" ("vrombissant"), "irritating" ("agaçant"), "sputtering" ("pétaradant"), "dynamic" ("dynamique"), "annoying" ("gênant").

Procedure

The experimental procedure consisted of the rating of the noises using the semantic differential and a verbalization task. Prior to the start of the test, the participants [START_REF] Botteldooren | Fuzzy models for accumulation of reported community noise annoyance from combined sources[END_REF] aggressive stressant (1) stressful agaçant [START_REF] Altinsoy | The semantic space of vehicle sounds: Developing a semantic differential with regard to customer perception[END_REF] irritating gênant [START_REF] Vogt | Simulation of tram noise abatement procedures in the psychoacoustic laboratory: A pilot study[END_REF] annoying dérangeant [START_REF] Goldsmith | Discord: The story of noise[END_REF] disturbing listened to all 14 stimuli in random order. Instructions were given verbally and were presented on the screen of the testing unit at the beginning of the test.

Furthermore, the meaning of each unipolar and bipolar rating scale was explained to the participants. They were introduced to the two different kinds of scales: for the bipolar scales, the mid-point was considered the neutral point whereas, for the unipolar scales, the mid-point represented a mean value for the peculiarity of the given adjective. The two different kinds of scales were separated visually. A screenshot of the user interface is presented in Figure 3.1. After the training phase, the participants had to rate each stimulus on the 25 item scales (cf. Section 3.2.4). The stimuli were presented in random order. Each stimulus could be played back as many times as required. The unipolar item "annoying" always was the last item of the semantic differential to be rated. After the annoyance scaling of each stimulus, the verbalization task followed. The participants were asked: "Can you describe why you give this annoyance rating?" ("Pouvez-vous dire pourquoi vous lui attribuez cette note de gêne"). Participants typed their responses using a keyboard and after validation they were able to proceed with the next stimulus. The duration of the experiment varied between 60 and 90 minutes.

Results

The assumption based on literature results that the duration has no effect on annoyance judgments was confirmed by a correlation analysis. Spearman's rank correlation coefficient (r s ) calculated between the stimulus duration and the median values for the item "annoying" was found to be non-significant (r s = 0.17; p = 0.55).

Dimensions of the perceptual space highlighted through the semantic differential

In order to identify the dimensions underlying the perceptual space of the tested urban road single-vehicle pass-by noises, a principal component analysis (PCA) with varimax rotation was employed. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.87, above the recommended threshold 0.6 [START_REF] Field | Discovering statistics using SPSS for windows[END_REF], and Bartlett's test of sphericity was significant (χ 2 (300) = 2879.08; p < 0.001). These criteria support the adequacy of the PCA. On the screeplot, the point of inflexion could be observed for four components which together explained 58 % of the overall variance (Table 3.3). The components can be interpreted based on the magnitude of the factor loadings obtained from the rotated component matrix (see Table 3.4).

Tabachnick and Fidell [START_REF] Tabachnick | Using Multivariate Statistics[END_REF] suggest a cut-off point of 0.32 for the minimum loading of an item. As can be seen, the first principle component is mainly associated with items referring to hedonic judgments, i.e. "disturbing", "annoying", "pleasant/unpleasant", "aggressive", "irritating". This component will be labelled "hedonic judgments" in the following. The second component is generally associated with items referring to temporal characteristics, i.e. "staccato", "sputtering", "smooth/rough", "humming", "intermittent/continuous", "hard/soft", "variable/constant" which will be named "temporal characteristics" hereafter. The third component has high loadings for items referring to spectral features, i.e. "bright", "hissy", "shrill/dull", "deep/sharp", "metallic", "tonal". This component will be named "spectral features". The fourth component is principally associated with items referring to the movement of the source and the driving behaviour, i.e. "slow/fast", "dynamic" and "nasal". The greatest amount of variance is explained by hedonic judgments. The sensation "nasal" is usually encountered for vehicles accelerating over a long period. The remaining portions of variance are mainly explained by acoustical features. Temporal changes, such as "staccato" and "sputtering" characteristics, explain a greater amount (12.2 %, cf. Table 3.3) of the variance in the data set than spectral features like "bright" and "shrill/dull" (6.6 %).

Interestingly, some items have factor loadings at 0.32 or higher on multiple principal components ("cross-loading items", cf. [START_REF] Costello | Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis[END_REF]). Interpretations drawn from cross-loading items could give indications on the influence of certain items on different components. An influence of temporal changes and spectral features on hedonic judgments can be observed: The items "smooth/rough", "hard/soft" and "shrill/dull" have strong primary loadings on the second and third principal component but also cross-loadings above and slightly below the cut-off point 0.32 [START_REF] Tabachnick | Using Multivariate Statistics[END_REF] on the first principal component. Regarding the second component, moderate cross-loadings can also be found for the items "irritating" and "metallic". The item "nasal'" loads strongly onto principal component 4 representing the "movement of the source" but also on component 3 representing spectral features. This suggests that the sensation "nasal" could also be associated by the participants with the perception of high frequencies.

These results can be related to findings from other studies (e.g. [START_REF] Namba | Loudness and timbre of broad-band noise mixed with frequency-modulated sounds[END_REF][START_REF] Kubo | Categorisation of engine sound[END_REF][START_REF] Kuwano | Quality of door sounds of passenger cars[END_REF]). The first principal component associated with hedonic judgements can be related to stimulus evaluation (e.g. [START_REF] Namba | Loudness and timbre of broad-band noise mixed with frequency-modulated sounds[END_REF][START_REF] Kubo | Categorisation of engine sound[END_REF][START_REF] Kuwano | Quality of door sounds of passenger cars[END_REF]). The second and third component associated with temporal and spectral signal features, respectively, can be related to the metallic/timbre factor in literature (e.g. [START_REF] Namba | Loudness and timbre of broad-band noise mixed with frequency-modulated sounds[END_REF][START_REF] Kubo | Categorisation of engine sound[END_REF][START_REF] Kuwano | Quality of door sounds of passenger cars[END_REF]). The fourth component is associated with items referring to the movement of the source and the way of driving and may be labelled "activity". Such a factor is also prevalent in other studies (e.g. [START_REF] Osgood | Cross-Cultural Universals of Affective Meaning[END_REF]).

Perceptual features highlighted through the verbalization task

The descriptive terms collected in the verbalization task highlight different perceptual features which were categorized according to three main categories: source identification, acoustical features and hedonic judgments. Special attention was laid on descriptive terms related to acoustical features. They were divided into terms related to spectral and temporal features of the noises. The following analy-sis of the verbalization data is based on the total number of occurrences (occ.) of the descriptive terms given by the 14 participants for the 14 studied stimuli.

Descriptive terms related to spectral features of the noises

Participants described their annoyance judgments relating to the spectral content of the urban road single-vehicle pass-by noises by using the terms "shrill" (15 occ.), "dull" (5 occ.),"sharp" (16 occ.), "deep" (3 occ.) and "hissy" (8 occ.), most often. The descriptive terms used to explain annoyance ratings and relating to the spectral features are presented in Table 3.5 along with their number of occurrences. The total numbers of occurrences of the descriptive terms "dull/shrill" and "deep/sharp" are 20 and 19, respectively. This indicates a consensus among the participants. To assess the relationship between "dull/shrill" and "deep/sharp", the correlation coefficient r s is computed using the median values for these items obtained from the semantic differential test. The correlation is strong (r s = 0.81; p < 0.001). This suggests that both items describe nearly similar sensations. For subsequent analyses, the item "dull/shrill" is used. The relationship between the item "dull/shrill" and the item "annoying" is also assessed by a correlation analysis using median values. It revealed a positive correlation (r s = 0.55; p < 0.05). This positive correlation accords with the verbalization data and confirms that the low/high-frequency content of the noises plays a role in the context of noise annoyance.

Descriptive terms related to temporal features of the noises

Regarding the temporal features of the noises, the participants described their annoyance judgments by referring to the following terms most frequently: "nasal" (14 occ.), "sputtering", (11 occ.), "staccato"(9 occ.), "constant" (9 occ.), "fluid" (9 occ.), "intermittent" (7 occ.) and "continuous" (7 occ.). Table 3.6 shows the descriptive terms used to explain annoyance ratings relating to modulation characteristics along with their number of occurrences.

As can be seen from Table 3.6, participants described that certain light vehicles, powered-two-wheelers and heavy vehicles were specifically annoying due to their "sputtering" sound characteristics (e.g. "two-wheeled vehicle backfiring during phase of acceleration": "Véhicule deux-roues pétaradant en phase d'accélération", "very sputtering noise": "bruit très pétaradant", "the effect of acceleration and the sputtering are annoying": "l'effet d'accélération et la pétarade sont gênants"). The "sputtering" sound characteristic was as well described as a "repetitive noise" ("bruit répété") and "low-frequency modulation" ("modulation en basses fréquences"). The adjective "nasal" was mentioned most often when the participants described their annoyance ratings for certain powered-two-wheelers at constant speed or accelerating over a long period (e.g. "Scooter with a noise like a buzzing of a wasp": "Scooter avec un bruit de bourdonnement de guêpe"). Participants seemed to have found consensus explaining their annoyance ratings by using the terms "nasal" and "sputtering". The relevance of the "nasal" and "sputtering" sensations to annoyance is tested by a correlation analysis by considering the median values of the ratings given by the participants on each scale. Both the median values for the items "sputtering" and "nasal" correlate significantly with the median values for the item "annoying" (r s = 0.68; p < 0.01 and r s = 0.59; p < 0.05, respectively). This confirms statistically that both sensations are associated with annoyance.

An accurate physical and perceptual characterization of "sputtering" and "nasal" sensations could contribute to an improved noise annoyance assessment.

Physical and perceptual characterization of auditory attributes

The analysis of the verbalization task highlights the importance of the auditory attributes related to spectral features ("dull/shrill") and to temporal characteristics ("sputtering" and "nasal") regarding annoyance judgements.

Characterization of spectral noise features

In order to characterize the sensation "dull/shrill" related to the spectral distribution of the acoustical energy, the psychoacoustic indices maximal sharpness (S max ), mean sharpness (S mean ) and sharpness percentiles (S 5 , S 10 , S 50 , S 90 , S 95 ), were computed. Percentile values are determined in order to take into account the sensitivity of human hearing to variations in time and to prominent sound events. Furthermore, the acoustic indices L HF were determined, which represent the maximal (across time) A-weighted sound pressure level in high frequencies obtained from the third-octave-band spectrum between 1.6 kHz and 12.5 kHz [START_REF] Alayrac | Annoyance from industrial noise: indicators for a wide range of industrial sources[END_REF].

In addition, the psychoacoustic index TETC [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF] was calculated; this index has been proposed to account for the high-frequency content due to squeal noise of single tramway pass-bys [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF]. In the current study, the TETC index is expected to characterize the breaking noise of urban road single-vehicle pass-by noises potentially influencing the sensation "dull/shrill". The index represents the Total Energy of the Tonal Components in high critical bands from 16 to 24 Barks for the studied urban road single-vehicle pass-by noises. Based on Eq. 2.1 this index is defined as follows:

T ET C = 10 log 10 where L(z) represents the maximal (across time) level of the tonal components as a function of the critical-band rate z. The maximal tonal component level as a function of the critical-band rate is calculated from the auditory spectrogram cf. Appendix A.1 and A.2). The TETC index is calculated based on this representation by summing the energy of the tonal components within critical bands from 16 to 24 Barks. L(z) and sharpness indices were computed using the software dBSonic© 01dB-Metravib. The implemented algorithm for sharpness calculation is based on Aures' model (cf. [START_REF] Geissner | Perception du bruit extérieur d'un véhicule urbain de livraison (Perception of exterior noise of an urban delivery truck)[END_REF]). L HF and TETC were calculated using MATLAB©. Maximal values across left and right channels are used for subsequent analyses.

Table 3.7 shows the correlation coefficients between the median values for the item "dull/shrill" and the values of the corresponding indices. The tonal components in high frequencies likely enforced the "dull/shrill" sensation of the noises. Figure 3.2 illustrates the good agreement between the computed TETC values and the median values for the item "dull/shrill". As seen in Figure 3.2, the single pass-by noise pdo_6, a heavy vehicle in deceleration, is judged and described by the participants as particularly shrill due to the presence of breaking noise (median value on the item scale "dull/shrill" = 7; 3 occurrences of the term "shrill" in the verbalization task and 2 occurrences of terms, such as "hissing" and "sharp noise", cf. Table 3.5). This is also represented by a high value of the TETC index.

From Table 3.7, it can also be seen that the TETC index correlates well with the median values for the item "annoying". As suggested by some studies (e.g. [START_REF] Kaczmarek | Annoyance of time-varying road-traffic noise[END_REF]), sharpness may have limited relevance for the characterization of annoyance from urban road traffic noises. Thus, the TETC index may be better suited for the characterization of the annoyance-relevant sensation "dull/shrill" due to urban road traffic noises. Furthermore, the results indicate the potential of the TETC index to be used to characterize the high-frequency content of different urban transportation noises such as urban road single-vehicle pass-by noises and single tramway noises [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF].

Characterization of temporal noise features

As sputtering and nasal pass-by noises were either directly associated with amplitude modulations (e.g. "low-frequency modulation") or associated with sensations that could involve amplitude modulations (e.g. "buzzing of a wasp"), the psychoacoustic indices fluctuation strength (F ) and roughness (R) are determined. The indices are calculated for the 14 stimuli using the software dBSonic© 01dB-Metravib.

The software description states that the implemented models for the calculation of roughness and fluctuation strength indices are based on Aures' models [START_REF] Aures | Ein Berechnungsverfahren der Rauhigkeit (A procedure for calculating auditory roughness)[END_REF]. Both indices are calculated for each ear; maximal values across left and right channels are considered for subsequent analysis. Indices are determined using maxima (R max , F max ), means (R mean , F mean ) and percentiles (R 5 , R 10 , R 50 , R 90 , R 95 and F 5 , F 10 , F 50 , F 90 , F 95 ). The results of the correlation analysis (Spearman's rank correlation coefficient) between the index values and the median values for the items "sputtering" and "nasal" are presented in Table 3.8. Only the values of the fluctuation strength indices significantly correlate with the median values for the items "sputtering" and "nasal". No significant correlations are observed between the roughness indices and both items. Figure 3.3 illustrates the median values for the item "sputtering" plotted against the F max values.

The correlation analysis between the median values for the item "sputtering" and the F max values yielded a significant positive correlation (r s = 0.63; p < 0.05). However, as can been seen from Figure 3.3, these results must be treated with caution. The single pass-by noises dfu_10 and dao_2 are calculated by this index as the most sputtering. This is not in agreement with the results from the semantic differential test for the item "sputtering" and from the verbalization task. According to the analysis of the verbalization task data, the modulation sensation "nasal" appears to be dominant for both noises (6 occ. for dao_2 and 5 occ. for dfu_10). In the semantic differential test, high ratings for the item "sputtering" were highlighted especially for dau_5 (median value on the item scale "not at all sputtering/extremely sputtering" = 6). However, this pass-by noise is not calculated by the index F max as the most sputtering pass-by noise.

Concerning the sensation "nasal", the correlation coefficient between F 10 and the median values for the item "nasal" is maximal. The noises dao_2 and dfu_10 are characterized by this index as very "nasal" (cf. Figure 3.4). However, dau_5 for example is calculated as nasal, too, which is in contradiction to the results obtained from the semantic differential test and the verbalization task. Thus, the calculated fluctuation strength is not sufficiently suitable to calculate the prominence of these two sensations that are linked to envelope variations of the noises. None of these psychoacoustic indices (F, R) was correlated with the median values for the item "annoying" (cf. Table 3.8) whereas the results of the verbalization task indicate the existence of such a relationship between annoyance and the sensations "sputtering" and "nasal".

In order to further investigate this outcome, an analysis of the signals' envelope is carried out to study the envelope characteristics of the single pass-by noises. As an example, Figure 3.5 shows the modulation spectra of two single pass-by noises described and rated as "sputtering" (Figure 3.5(a), (b)), and of two single pass-by noises described and rated as "nasal" (Figure 3.5 (c), (d)). Roughness appears in the range of modulation frequencies between 15 Hz and 300 Hz with a maximum at 70 Hz [START_REF] Fastl | Psychoacoustics -Facts and Models[END_REF]. The difficulty in characterizing modulation sensations evoked by road vehicle pass-by noises lies in the spectral distribution of amplitude modulations. A possible explanation for the failure of the roughness indices might be the fact that current roughness models are developed based on signals with sinusoidally shaped envelopes [START_REF] Fastl | Psychoacoustics -Facts and Models[END_REF]. Environmental noises, such as the single pass-by noises used in this study, however, exhibit envelopes with complex shapes. Both fluctuation strength and roughness sensations could be present and the classical psychoacoustic indices appear to be not adapted to account for the modulation sensations evoked by urban road single-vehicle pass-by noises.

Proposal of two indices characterizing different modulation-related sensations

In the following, the philosophy behind the indices for characterizing the prominence of "sputtering" and "nasal" sensations is outlined.

"Sputtering" and "nasal" indices

An investigation of the urban road single-vehicle pass-by noises showed that those noises that are characterized as "sputtering" exhibited strong components in the modulation frequency spectrum of the envelopes below 100 Hz whereas the nasal pass-by noises had strong frequency components in the modulation frequency range from 100 Hz to 200 Hz. The influence of these frequency components on variations of the envelope depends on their relative magnitude compared to the dc-value. The "sputtering" and "nasal" indices are modulation indices determined from different modulation frequency bands. As these indices are calculated from the modulation spectrum, it will be shown first how a general modulation index can be expressed by Fourier components of the modulation spectrum.

Let the time signal of the amplitude-modulated pressure p be given by

p(t) = env(t) • p c (t) (3.2)
where p c (t) is a carrier signal (e.g. a sinusoid) and env(t) is a time-varying envelope. We further assume that the time-varying envelope is sinusoidal, e.g.

env(t) = p 0 (t) • (1 + m • cos(ω mod t + ϕ)) (3.3)
where p 0 is the mean envelope, m the modulation index and ω mod the modulation frequency. Because of the relationship

cos(α) = 1 2 • e iα + 1 2 • e -iα (3.4)
the envelope env(t) can be developed into its Fourier components:

env(t) = p 0 + p 0 •m 2 • e i(ω mod t+ϕ) + p 0 •m 2 • e -i(ω mod t+ϕ) (3.5) = p 0 + p 0 •m 2 • e iϕ • e iω mod t + p 0 •m 2 • e -iϕ • e -iω mod t (3.6)
The general representation of env(t) as a sum of Fourier components is given by env(t) = P (0) + P (ω mod ) • e iω mod t + P (-ω mod ) • e -iω mod t (3.7)

with P (0), P (ω mod ) and P (-ω mod ) as Fourier components for the frequencies ω = 0, ω mod and -ω mod .

A comparison of Eqs. 3.6 and 3.7 yields:

P (0) = p 0 (dc-value) (3.8)
P (ω mod ) = p 0 •m 2
• e iϕ (3.9)

P (-ω mod ) = p 0 •m 2 • e -iϕ (3.10)
To express the modulation index m in terms of the Fourier components, the absolute value of P (ω mod ) is considered:

|P (ω mod )| = p 0 • m 2 • e iϕ = p 0 • m 2 = P (0) • m 2 (3.11) m = 2 • |P (ω mod )| P (0) (3.12)
Throughout a single pass-by, "sputtering" and "nasal" sound characteristics can appear either sporadically or they can be constantly audible. This was described by the participants in the verbalization task (e.g. "sputtering at the beginning"). In order to extract parameters that refer to the temporal evolution of these sensations, the "sputtering" and "nasal" modulation indices are estimated within successively overlapping time frames. The frame length was set to T = 400 ms and the step size to 23 ms. The indices are computed on the basis of the modulation spectra that are calculated using the instantaneous amplitude of the analytical signals that are build within the pass-by noise time frames.

The single pass-by noises used in this study comprise multiple modulation frequencies appearing in the range [0, 200] Hz (cf. Figure 3.5). For the estimation of the "sputtering" and "nasal" modulation indices, the most prominent modulation frequency components are considered. Corresponding to the observations that are illustrated in Figure 3.5 in Section 3.3.3, the "sputtering" index m sputt is calculated by using the amplitude of the Fourier coefficient |P max (2Hz -100Hz)| with the highest peak in the frequency range from 2 Hz to 100 Hz within a time frame i:

m sputt i = 2 • |P max (2Hz -100Hz)| P (0) i (3.13)
The d.c. component P (0) appears in the modulation spectrum in the range [0, 1.5] Hz (cf. Figure 3.5).

The "nasal" modulation index m nas is calculated in a similar way by determining the amplitude |P max (100Hz -200Hz)| of the strongest modulation frequency component in the range from 100 Hz to 200 Hz within a time frame i:

m nas i = 2 • |P max (100Hz -200Hz)| P (0) i (3.14)
The indices are calculated for both the left and right channels but only maximal values across both channels are used.

In order to account for fast changes, the final indices to characterize the timevarying "sputtering" and "nasal" sound characteristics are the 90 th percentiles of the frame-dependent modulation indices, i.e. the values of the indices that are exceeded in 10 % of the time. In the following, they are denoted as • m sputt,10 for the "sputtering" index, • m nas,10 for the "nasal" index of a pass-by noise.

Relationship between the proposed modulation indices and item judgments

In this section, the two proposed indices, m sputt,10 and m nas,10 , are calculated for the 14 single pass-by noises and compared with the ratings for the items "sputtering", "nasal" and "annoying" obtained in the semantic differential test.

"Sputtering" index m sputt,10

To assess the relationship between the median values for the item "sputtering" and the "sputtering" index values m sputt,10 calculated for the different single pass-by noises, a correlation analysis is conducted. The correlation analysis yields a positive and significant correlation coefficient (r s = 0.78; p < 0.01). Figure 3.6 illustrates the median values for the item "sputtering" plotted against the calculated "sputtering" index values m sputt,10 .

The single pass-by noise rated as the highest sputtering noise (dau_5) is characterized as the highest sputtering noise. However, the "sputtering" character of the noises vau_5 and dao_2 is underestimated.

The contribution of the proposed index m sputt,10 to noise annoyance is tested. The values of the index correlate significantly with the median values for the item "annoying" (r s = 0.61; p < 0.05). The index m sputt,10 correlates better with both the items "sputtering" and "annoying", compared to roughness and fluctuation strength tested in Section 3.3.3.2.

"Nasal" index m nas,10

The correlation analysis between the median values for the item "nasal" and the "nasal" index values m nas,10 yields a high positive and significant correlation coefficient (r s = 0.81; p < 0.001). This result is summarized in Figure 3.7.

The single pass-by noises rated as the highest nasal noises (dao_2 and dfu_10) are characterized as the highest nasal noises. The "nasal" index m nas,10 , however, overestimates the "nasal" character of the noise ddo_1 in relation to its corresponding median value for the item "nasal". Regarding this single pass-by noise, both "sputtering" and "nasal" sound characteristics are audible and represented by high values of the two proposed indices. As the pass-by noise ddo_1 is decelerating and approaching, it has a "nasal" sound characteristic only for a very short period of time before starting to slow down. During deceleration, the gear is changed twice which is clearly audible and results in a pronounced "sputtering" sound characteristic. This is well represented by the proposed index m sputt,10 (cf. Figure 3.6). As the "sputtering" sound characteristic is more dominant in terms of its presence over time, the participants very likely evaluated this noise little nasal. Thus, the proposed index may overestimate the "nasal" character of a noise in presence of a low-frequency modulation sensation. Both, the "sputtering" and "nasal" indices could possibly be used to construct an annoyance indicator in order to take into account the relative contribution of both sensations to annoyance.

The values for the "nasal" index m nas,10 correlate significantly with the median values for the item "annoying" (r s = 0.6; p < 0.05). The median values for the item "nasal" plotted against the "nasal" index values (m nas,10 ) for the 14 urban road single-vehicle pass-by noises.

The index m nas,10 correlates better with both the items "nasal" and "annoying", compared to roughness and fluctuation strength indices tested in Section 3.3.3.2.

Discussion

Prior work [START_REF] Vos | Noise annoyance caused by mopeds and other traffic sources[END_REF][START_REF] Paviotti | On the outdoor annoyance from scooter and motorbike noise in the urban environment[END_REF][START_REF] Morel | Physical and perceptual characterization of road traffic noises in urban areas for a better noise annoyance assessment[END_REF] has highlighted the importance of specific noise characteristics in terms of temporal and spectral features in the context of noise annoyance evoked by urban road single-vehicle pass-by noises. In this study, based on a semantic differential test and a verbalization task, we identified and characterized annoyance relevant noise features for a selection of 14 urban road single-vehicle pass-by noises. The noises were chosen according to representativeness ratings within a typology of 57 single urban road vehicle pass-by noises recorded in situ [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF].

Based on the analysis of the verbalization task, the sensation "dull/shrill" and the modulation-related sensations "sputtering" and "nasal" were found to be relevant in the context of noise annoyance evaluation. Moreover, the importance of descriptive terms to characterize noise features was in line with the study by Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF].

In the current study, the sensation "dull/shrill" correlated well with the TETC index used to characterize the high-frequency content mostly due to breaking noise. The importance of spectral features was also demonstrated in previous studies [START_REF] Ishiyama | The impact of sound quality on annoyance caused by road traffic noise: an influence of frequency spectra on annoyance[END_REF][START_REF] Versfeld | Annoyance caused by sounds of wheeled and tracked vehicles[END_REF]. They showed that the high-frequency content of road traffic noise significantly contributes to noise annoyance. Sharpness values also correlated with the median values for the items "dull/shrill" and "annoying". Although, these findings are consistent with Ishiyama and Hashimoto [START_REF] Ishiyama | The impact of sound quality on annoyance caused by road traffic noise: an influence of frequency spectra on annoyance[END_REF], they differ with those of Kaczmarek and Preis [START_REF] Kaczmarek | Annoyance of time-varying road-traffic noise[END_REF] who found that sharpness of their selected noises did not correlate with annoyance. The TETC index turns out to be better adapted to characterize the high-frequency content of different urban transportation noises such as single pass-by noises of road vehicles and tramways. This index has been applied successfully to characterize the high-frequency content of single tramway pass-by noises [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF].

Regarding the temporal aspects of the noise signals, we did not find studies aiming at the improvement of the characterization of perceptual features related to amplitude modulations evoked by urban road single-vehicle pass-by noises. "Sputtering" and "nasal" sound characteristics were highlighted by the participants in the verbalization task. Modulation analysis revealed that the spectral distribution of amplitude modulations in the range [0,200] Hz plays an important role for the modulation-related sensations: sputtering pass-by noises exhibited strong components in the modulation frequency spectrum below 100 Hz whereas the nasal pass-by noises had strong frequency components in the modulation frequency range from 100 Hz to 200 Hz. The values of the proposed "sputtering" index m sputt,10 and the "nasal" index m nas,10 correlated well with the results from the semantic differential test and were in line with the verbalizations provided by the participants to explain their annoyance ratings. In this work, no correlation was found between the calculated values of the psychoacoustic index roughness and the median values for the item "nasal", even though this item was associated with modulation frequencies in the range of the roughness sensation defined in Fastl and Zwicker [START_REF] Fastl | Psychoacoustics -Facts and Models[END_REF]. Up to now, it is not clear whether current fluctuation strength and roughness models can adequately deal with changes in the observed modulation frequencies, that occur during a pass-by. Also, even though modulation sensations were described as "annoying" in the verbalization task, positive relationships between the calculated indices roughness and fluctuation strength and the annoyance responses could not be established. This finding corroborates the results of Paviotti and Vogiatzis [START_REF] Paviotti | On the outdoor annoyance from scooter and motorbike noise in the urban environment[END_REF], who concluded that roughness and fluctuation strength do not seem to be adapted for the characterization of annoyance evoked by powered-two-wheeler and light vehicle pass-by noises. This could be attributed to the complex shape of envelopes of these transportation noises and to the fact that current fluctuation strength and roughness models are generally not able to consider the influence of the shape of the envelope on roughness. They were developed for sinusoidal modulation signals [START_REF] Aures | Ein Berechnungsverfahren der Rauhigkeit (A procedure for calculating auditory roughness)[END_REF]. Although the two proposed indices m sputt,10 and m nas,10 , do not consider the special form of the envelope either, the statistical determination of the indices turned out to give a meaningful characterization of envelope features in accordance with the verbalization task: single pass-by noises judged as "sputtering", such as the powered-two-wheelers dau_5 and ddo_1, are characterized as the most "sputtering" noises, and single pass-by noises judged as "nasal", such as the powered-twowheelers dao_2 and dfu_10, are characterized as the most "nasal" noises. Single pass-by noises which exhibit both "sputtering" and "nasal" sound characteristics are likely to be well represented by just one of the proposed indices. For the powered-two-wheelers ddo_1, the "sputtering" sensation was more dominant over time compared to the "nasal" sensation. This is not yet taken into account by the proposed indices. This suggests that an annoyance indicator constructed based on multiple linear regression using both indices could potentially be used to take into account the relative contribution of both sensations to the annoyance felt.

Conclusion

From the results obtained with 14 urban road single-vehicle pass-by noises, the following conclusions are drawn:

1. Four dimensions of the multidimensional perceptual space were identified referring to hedonic judgments, the temporal and spectral characteristics of the noises and the movement of the source. These dimensions could be meaningfully related to previous work. From the verbalization task, the descriptive terms "dull/shrill", "sputtering" and "nasal" emerged; the median values for the corresponding items correlated positively with the median values for the item "annoying".

2. The TETC index proved to be highly adequate for the characterization of the sensation "dull/shrill" and "annoying". This index was proposed in recent literature to characterize noise annoyance evoked by high-frequency content of single tramway pass-by noises; this suggests that the same index may be used to characterize different urban transportation noises.

3. A characterization of modulation-related sensations inducing annoyance was carried out using the proposed "sputtering" index m sputt,10 and "nasal" index m nas,103 . The results are meaningful compared to the characterization using psychoacoustic models of fluctuation strength and roughness. This characterization was congruent with the results of the verbalization task.

It has been shown that it is possible to identify specific noise characteristics that affect noise annoyance and to develop suitable signal parameters that highly correlate with the perceived annoyance relevant noise characteristics. This approach is suited to improve noise annoyance models such that they additionally consider specific sound characteristics that contribute to the annoyance reported.

The results are promising as the proposed indices in the current study may complete the energy-based index L den required for noise maps. A further study should therefore first apply the proposed indices using a wider variety of single pass-by noises with level differences between vehicles. This would be the purpose of the next chapter.

Chapter 4

Application of the relevant indices to different urban road vehicle pass-by noises

In this chapter, the previously proposed modulation indices will be tested by considering a broad range of urban road vehicle pass-by noises in different driving conditions. This test will be carried out for various types of urban road vehicles without and with level differences between vehicles. The methodology and results of two experiments will be described. Parts of the work described in this chapter complement the articles

Introduction

In order to test the indices proposed in the previous chapter, two further experiments are carried out based on a higher number of road vehicle pass-by noises differing in vehicle types and driving conditions. In Experiment 1 (Exp. 1), 33 pass-by noises equalized to the same L Aeq were used. In Experiment 2 (Exp. 2), the same pass-by noises were employed but mean level differences were applied to each pass-by noise in order to take into account level differences between vehicles as observed in situ. These mean level differences were determined from in situ recordings [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF]. The objective of this work was to first test the proposed indices m sputt,10 and m nas,10 for the characterization of noise annoyance considering a higher number of vehicles (Experiment 1) and secondly to assess their relevance in combination with other indices when level differences are considered between vehicle pass-by noises (Experiment 2).

Experimental methodology

The experimental methodologies of both experiments is identical. They differ only in the stimuli employed.

Stimuli used in Experiment 1

The 57 pass-by noises recorded by Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF] were divided into seven perceptual and cognitive categories (Category 1: powered-two-wheelers at constant speed; Category 2: powered-two-wheelers in acceleration; Category 3: buses, heavy and light vehicles at constant speed; category 4: powered-two-wheelers in deceleration; Category 5: buses, heavy and light vehicles in deceleration; Category 6: light vehicles in acceleration; Category 7: buses and heavy vehicles in acceleration). To limit the number of stimuli for Exp. 1, 33 pass-by noises were selected from the perceptual categories based on the following criteria: (i) for categories consisting of four pass-by noises, all the pass-by noises were chosen; (ii) regarding pass-by noises from categories comprising a larger number of stimuli, a maximum of five pass-by noises per category was selected according to their note of category representation determined by Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF]. The total corpus of stimuli consists of powered-two-wheelers, buses, heavy vehicles and light vehicles in different driving conditions. Their durations range from approximately 3 s to 9 s. The stimuli are equalized to the same L Aeq of 60 dB(A). They are equalized to the same average between the left and right channel. Thus, original level differences between the channels are kept. Auditory spectrograms of pass-by noises used in Experiment 1 are provided in Appendix A.1 to A.3.

Stimuli used in Experiment 2

The same 33 pass-by noises also considered in Exp. 1 are employed but mean level differences are applied to each pass-by noise corresponding to mean level differences measured in situ between the different types of vehicles and their driving conditions [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF]. These levels were obtained by the measurement procedure summarized below [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF]:

1. For each recorded pass-by noise, the level differences (∆ vfo ) are determined between the average sound pressure levels recorded for the light vehicles at constant speed (vfo) and the average sound pressure levels measured for other vehicles in other driving conditions.

2. Then, the level differences ∆ vfo are averaged for every measurement point in order to obtain mean level differences between the light vehicles at constant speed and the different vehicle types under different driving conditions.

The reference level for light vehicles at constant speed was set to 54 dB(A) in Exp. 2 in order to obtain a sound pressure level range acceptable for listeners. From this level, the mean level differences ∆ vfo were applied to the left and right channel of each pass-by noise depending on the vehicle types and driving conditions according to Table 4 The level differences were applied to each channel in order to keep the original level differences between the channels. The resulting sound reproduction levels for the different pass-by noises ranged from 49 dB(A) to 62.5 dB(A).

Apparatus

The sound reproduction system for both experiments consisted of the same set-up which was also employed in the experiment described in Chapter 3. The user interface was coded using Matlab©.

Procedure

The two experiments were performed one after another within the same test. In each experiment, the order of the presentation of the stimulus was randomized. The instructions were presented on the screen and were also repeated verbally. A screenshot of the instructions presented on the testing unit in the original French language is shown in Figure 4.1. The participants were asked to imagine themselves at home while relaxing (e.g. reading, watching television, discussing, gardening or doing other common relaxing activities). Prior to each experiment, the participants were trained. For each stimulus, a reminder of the imaginary situation was presented to the participants and the phrase was added: "During your relaxing activity, you hear this noise". Then, the following question was asked: "Does this noise annoy you?". The response scale was derived from the recommendations given in Standard ISO 15666 [START_REF]Acoustics -Assessment of noise annoyance by means of social and socio-acoustic surveys[END_REF]. The participants were asked to move a slider along a continuous scale ranging from "0" to "10", with 11 evenly spaced numerical labels and two verbal labels one at each end ("not at all annoying" and "extremely

Participants

The test comprising the two experiments was undertaken by 34 participants (17 male and 17 female), aged between 20 and 54 years (mean age = 32.5, standard deviation = 11.8). All participants declared normal hearing abilities and were paid for their participation. In order to evaluate the potential effect of the experiment order (Exp. 1 followed by Exp. 2 or the reverse), the panel of participants was divided into two groups. One group consisting of 17 participants performed Exp. 1 and then participated in Exp. 2. The second group of participants carried out the two experiments in reverse order.

Statistical analysis

For each experiment, the analysis followed a similar procedure: First, interindividual differences were analyzed and potential outliers and/or subgroups identified. This was followed by a repeated measures analysis of variance (RM ANOVA). Mean annoyance responses were characterized using the Pearson correlation coefficient and graphical representations. In Exp. 2, partial correlation coefficients were calculated in order to identify indices which contribute to the annoyance characterization in addition to mean loudness. Finally, linear multiple regression analysis was carried out in order to assess the relevance of combining the proposed indices in the perspective of constructing a noise annoyance indicator for urban road traffic noise.

Results

Analysis of order effect

First, the effect of the order of presentation of the experiments within the test was assessed. Two-factor mixed-design ANOVAs (with one within-subject factor "Stimulus" and one between-subject factor "Order") were carried out on the annoyance responses obtained in Exp. 1 and Exp. 2, respectively. Concerning Exp. 1, the mixed-design ANOVA yielded a non-significant effect of the experiment order [F(1,32) = 0.57; p = 0.45]. For Exp. 2, there was also a non-significant effect of the experiment order [F(1,32) = 2.15; p = 0.15]. Hence, the annoyance responses from the 34 participants can be grouped together in order to analyze the responses respectively gathered in Exp. 1 and Exp. 2.

Experiment 1

In Exp. 1, noise annoyance due to the 33 stimuli equalized to an A-weighted equivalent sound pressure level of 60 dB(A) was tested. The pass-by noises were equalized to study the influence of spectral and temporal characteristics of the pass-by noises on noise annoyance. The focus of the analysis lies in the characterization of annoyance using the classical psychoacoustical indices compared to the proposed indices m sputt,10 and m nas,10 . The analysis of Exp. 1 comprises two steps: i) the characterization of influential acoustical/psychoacoustic factors on noise annoyance, and ii) the assessment of the combination of the proposed indices using multiple regression.

Analysis of interindividual differences

A hierarchical cluster analysis using correlation distances and the average linkage method was run on the 34 participants' annoyance responses in order to identify outliers and/or subgroups.

No outliers can be observed and the responses of the participants were considered to be homogeneous.

Analysis of inter-stimulus differences

A repeated measures ANOVA was carried out. The results show that the stimuli have a significant effect on the annoyance responses [F(10.14,334.5) = 15.47, p < 0.001]. The proportion of variance explained by the factor "Stimulus" is equal to 32 %. ) from which the pass-by noises stem [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF] are reported. In each perceptual category, the pass-by noise with the highest representation rating [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF] is shown in red characters.

It can be seen that there are clear differences between the different urban road vehicle pass-by noises equalized in level. The least annoying urban pass-by noises are a light vehicle at constant speed (vfo_5), a light vehicle in acceleration (vao_3) and a heavy vehicle at constant speed (pfu_2). The most annoying urban road vehicle pass-by noises are powered-two-wheelers in acceleration (dao_2, dao_3), poweredtwo-wheelers in deceleration (ddo_2, ddu_1) and heavy vehicles in deceleration (pdo_3, pdo_6).

Characterization of annoyance

The characterization of mean annoyance due to the 33 pass-by noises will be carried out in two steps: i) determination of relevant indices for the characterization of urban road vehicle pass-by noises ii) analysis of the relationships between the highlighted indices and mean annoyance responses.

Indices were calculated that account for the spectral characteristics of the noises (sharpness S, Total Energy of the Tonal Components within critical bands from 16 to 24 Barks TETC, the A-weighted sound pressure level in low (L LF ), mid (L MF ) and high frequencies (L HF ) calculated from the third octave spectrum (cf. [START_REF] Alayrac | Annoyance from industrial noise: indicators for a wide range of industrial sources[END_REF])). Indices representing the temporal characteristics of the noises linked to amplitude modulations are also shown (fluctuation strength F , roughness R, "sputtering" index m sputt,10 and "nasal'" index m nas,10 ). For the classical acoustic/psychoacoustic indices maxima, means, and values exceeded in 5, 10, 50, 90 and 95 % of the time were computed. Maximal values across left and right channels are used for subsequent analyses. The Bravais-Pearson correlation coefficients are illustrated in Table 4.2.

Spectral features Among the indices calculated taking into account the frequency content of the noises, such as sharpness and L HF and TETC, only the TETC index yielded a significant positive correlation with mean annoyance responses. To assess the relationship between this index and the mean annoyance responses, This indicates that for these pass-by noises the high-frequency content played an important role in the formulation of annoyance judgments. The poweredtwo-wheelers ddo_1, dao_2 and dao_3 are the most annoying pass-by noises but are not well represented by the TETC index. This suggests that for these pass-by noises other acoustical characteristics may have caused such high annoyance ratings. Table 4.2: Bravais-Pearson correlation coefficients ( a p < 0.001, b p < 0.01, c p < 0.05) calculated between mean annoyance ratings and the acoustic/psychacoustic indices calculated for 33 pass-by noises equalized to 60 dB(A). Indices are computed accounting for the spectral characteristics of the noises (A-weighted sound pressure level in low (L LF ), mid (L MF ) and high frequencies (L HF ) (cf. [START_REF] Alayrac | Annoyance from industrial noise: indicators for a wide range of industrial sources[END_REF]), sharpness S, Total Energy of the Tonal Components within critical bands from 16 to 24 Barks TETC) and the temporal characteristics of the noises linked to amplitude modulations (fluctuation strength F , roughness R, "sputtering" index m sputt,10 and "nasal" index m nas,10 ) The psychoacoustic indices were calculated using dBSonic 01dB-Metravib©.

Index

Unit

Modulation-related features Regarding the temporal features influencing

noise annoyance judgments, "sputtering" and "nasal" sound characteristics were highlighted for certain pass-by noises in the previous experiment including the verbalization task.

The relationship between the mean annoyance responses and the "sputtering" and "nasal" indices is of particular interest as the objective of this experiment is to confirm the characterization potential of the proposed indices. In this experiment, both m sputt,10 and m nas,10 calculated for the 33 pass-by noises correlate well with mean annoyance responses. Figure 4.5 illustrates the relationship between mean annoyance responses for the 33 pass-by noises and the values of each of these indices by means of a scatter plot.

The positive significant correlation between m sputt,10 and mean annoyance is summarized in Figure 4.5(a). The most annoying pass-by noise, dao_3, is characterized as the most "sputtering" noise by the index m sputt,10 . The other pass-by noises appear to be clustered about the regression line.

In Figure 4.5(b), the pass-by noises, dao_2 and dfu_10, are represented by the "nasal" index as the most annoying noises among the 33 pass-by noises. Thus, the "nasal" character likely evoked such high annoyance ratings. Also, the most "sputtering" noise, dao_3, is characterized as moderately "nasal". This suggests that a combination of both the "sputtering" and "nasal" indices could possibly be used to construct an annoyance indicator in order to take into account the relative contribution of both sensations to annoyance.

To assess the relationship between the psychoacoustic indices accounting for temporal characteristics of the noise linked to amplitude modulations, Figure 4.6 illustrates the mean annoyance responses plotted against the F mean index values, From Figure 4.6 it can be seen that both the F mean and the R max index produce little spread of data points along the regression line. Compared to Figure 4.5(a), the F mean index also characterizes dao_3 as being most annoying among the tested pass-by noises. The characterization based on F mean suggests further that the pass-by noises dao_2 and dau_5 are almost equally annoying due to prominent lowfrequency amplitude modulations. However, in both the semantic differential test and the verbalization task participants found that this modulation-related sensation was not relevant for dao_2 and in contrary highlighted its "nasal" character resulting in high annoyance ratings.

As shown in Figure 4.6(b), the index R max yields the same results in terms of the most annoying pass-by noise as the index F mean . Whereas dao_2 appears to be well represented by R max , the "nasal" pass-by noise dfu_10 is characterized as the least annoying pass-by noise. This is in contradiction to the findings from the semantic differential test and the verbalization task. Participants highlighted the particularly "nasal" character of dfu_10 resulting in high annoyance ratings. In the aim of constructing an indicator, the combination of these two indices should be investigated further.

These observations indicate that the characterization based on the proposed indices m sputt,10 and m nas,10 provide a more meaningful representation as they confirm the findings from the semantic differential test and the verbalization task.

Assessment of the combination of the proposed indices: Indicator construction

A linear multiple regression procedure was run to calculate the mean annoyance responses evoked by 33 pass-by noises equalized to 60 dB(A). Indices accounting for spectral and temporal features with the strongest correlation coefficients were entered into the different models to maximize the model fit. In order to assess the relevance of combining the highlighted indices for noise annoyance calculation, two models were created. Into the first model the following indices were inserted: m sputt,10 , m nas,10 and the TETC index. Into the second model the indices R max , F mean and the TETC index were entered. The final models and their corresponding model fit are presented in Table 4.3.

Experiment 2

In this experiment, the same 33 urban road vehicle pass-by noises were presented to the participants but to each pass-by noise level differences were applied corresponding to differences measured in situ (cf. Table 4.1). From Chapter 3 and the results of Exp. 1, it is known that spectral and modulation-related features play an important role in the context of noise annoyance judgments. In this experiment, an important auditory attribute, the loudness, was considered. It is known to be an important influential factor. The structure of this section is identical to the previous section. The characterization of mean annoyance responses is followed by the assessment of the combination of the highlighted indices.

Analysis of interindividual differences

A hierarchical cluster analysis was run on the 34 participants using correlation distances and the average linkage method. No outliers were highlighted, thus the group of participants appears homogeneous.

Analysis of inter-stimulus differences

A repeated measures ANOVA was conducted to determine whether the annoyance ratings given for the 33 non-equalized stimuli are significantly different. Differences between annoyance ratings for the stimuli are significantly different [F(10.36, 341.99) = 43.06, p < 0.001]. The proportion of variance explained by the factor "Stimulus" is equal to 57 %. There are clear differences between mean annoyance responses. Compared to the mean annoyance responses obtained in Exp. 1 larger differences in annoyance ratings can be observed. This could be due to the sound pressure level differences of the different pass-by noises. As for Exp. 1, the results show that the light vehicles are the least annoying vehicles and that the powered-two-wheelers, heavy vehicles and buses represent more annoying vehicles. Taking into account the mean sound pressure level between the different vehicles reinforced this tendency. ) from which the pass-by noises stem [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF] are reported. In each perceptual category, the pass-by noises with the highest representation rating [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF] is shown in red characters.

Characterization of annoyance

The characterization comprises two steps: i) determination of relevant indices relating to spectral features, modulation-related features and intensity; ii) analysis of the relationships between mean annoyance responses and these indices. As in the previous chapter, the focus of this chapter lies in the comparison between classical psychoacoustic indices and the proposed modulation indices.

Noise annoyance due to the 33 pass-by noises of Exp. 2 is characterized using the following indices: A-weighted sound pressure level L, loudness N, sound pressure level in low frequencies L LF , mid frequencies L MF and high frequencies L HF calculated from the third octave spectrum (cf. [START_REF] Alayrac | Annoyance from industrial noise: indicators for a wide range of industrial sources[END_REF]), the Total Energy of the Tonal Components within critical bands from 16 to 24 Barks TETC, sharpness S, fluctuation strength F , roughness R, "sputtering" index m sputt,10 and "nasal" index m nas,10 . The classical acoustic and psychoacoustic indices are determined using maxima, means and values exceeded in 5, 10, 50, 90 and 95 % of the time. The indices are calculated separately for each ear; maximal values across left and right channels are considered for subsequent analysis. Table 4.4 shows the Bravais-Pearson correlation coefficients between the mean annoyance responses and the values of the indices.

In contrast to Exp. 1, the sound pressure level differences of the pass-by noises Table 4.4: Bravais-Pearson correlation coefficients ( a p < 0.001, b p < 0.01, c p < 0.05) calculated between mean annoyance ratings and the acoustic/psychacoustic indices calculated for 33 pass-by noises with different sound pressure levels. Indices are computed accounting for the spectral characteristics of the noises (A-weighted sound pressure level in low (L LF ), mid (L MF ) and high frequencies (L HF ) (cf. [START_REF] Alayrac | Annoyance from industrial noise: indicators for a wide range of industrial sources[END_REF]), sharpness S, Total Energy of the Tonal Components within critical bands from 16 to 24 Barks TETC) and the temporal characteristics of the noises linked to amplitude modulations (fluctuation strength F , roughness R, "sputtering" index m sputt,10 and "nasal" index m nas,10 ) The psychoacoustic indices were calculated using dBSonic 01dB-Metravib©.

2).

A first inspection of Figure 4.8 shows that pass-by noises are generally well represented by the index as the data points are clustered close to the line. Two distinct groups, dependent on the type of pass-by noises, are apparent along the regression line. The light vehicles, forming one group, are the least annoying pass-by noises. The second group comprises buses, heavy vehicles and poweredtwo-wheelers which were rated as more annoying. The light vehicles also exhibit the lowest loudness compared to the other pass-by noises. The reproduction level of these stimuli is the lowest among all the stimuli and ranged between 48.5 dB(A) and 53 dB(A) compared to the sound reproduction level of the buses, heavy vehicles and powered-two-wheelers which ranged between 58 dB(A) and 62.1 dB(A).

In order to identify indices which contribute to the annoyance characterization besides mean loudness, partial correlation coefficients (r part , cf. [START_REF] Howell | Statistical Methods for Psychology[END_REF]) were calculated controlling for mean loudness. Among the indices characterizing spectral features, the TETC index yielded the strongest partial correlation coefficient with mean annoyance (r part = 0.52; p < 0.01). As shown in Figure 4.9, this index leads to a meaningful characterization highlighting the "shrill" character of the single pass-by noise pdo_6. This is in agreement with the results of the semantic differential test and of the verbalization task (cf. Section 3.3). Bravais-Pearson correlation analysis suggests a strong relationship between L HF and mean annoyance (r = 0.85; p < 0.001). However, when controlling for mean loudness, the partial correlation coefficient between L HF and mean annoyance proves to be not significant (r part = 0.23; p = 0.2). This indicates the strong dependency of L HF on mean loudness. For the characterization of irregular amplitude variations, the index STDP yielded a strong Bravais Pearson correlation coefficient. However, controlling for mean loudness, the partial correlation between STDP values and mean annoyance responses is not significant (r part = 0.14; p = 0.46). Thus, this index will not be considered for further analysis.

Considering the indices characterizing sensations related to low amplitude modulations, controlling for mean loudness, the m sputt,10 index yielded the strongest partial correlation coefficient in comparison to F 10 (m sputt,10 : r part = 0.62, p < 0.001; F 10 : r part = 0.43, p < 0.05). The "nasal" index m nas,10 was also significantly partially correlated with mean annoyance (r part = 0.4, p < 0.05). Bravais-Pearson correlation analysis suggests a strong relationship between R 95 and mean annoyance (r = 0.67; p < 0.001). This is, however, related to the loudness-dependency of R 95 as the partial correlation coefficient is not significant (r part = 0.05; p = 0.8). Considering the results of the semantic differential test and of the verbalization task, both indices contribute to the annoying character of the pass-by noises. For instance, based on m sputt,10 , the pass-by noise dau_5 is characterized more annoying due to low amplitude modulations than the "nasal" pass-by noise dao_2 (cf. Section 3.3.3.2). The m nas,10 index characterizes dfu_10 and dao_2 as very annoying due to their "nasal" characteristic (cf. Section 3.3.3.2). From Table 4.5 it can be seen that the best goodness-of-fit is obtained using Model 1 as both R2 and R 2 adj are maximal whereas SE is small. It must be noted that the regression coefficients are all highly significant for Model 1 compared to Model 2. Based on the standardized regression coefficients, the contribution of each variable to the model can be determined. Mean loudness contributes strongly to Model 1 (53 %), and the contribution of the other auditory attributes characterized by the indices m sputt,10 , m nas,10 and TETC is also important (47 %). This illustrates the relevance of the proposed indices besides mean loudness2 .

Discussion of Experiment 1 and Experiment 2

For both experiments, the repeated measures ANOVA showed that differences between annoyance ratings are significant. The analysis of the graphical representation indicates that the annoyance responses were influenced by the vehicle type and driving condition. Even for Exp. 1 employing equalized stimuli, light vehicles were found in general to be less annoying than heavy vehicles and powered-two-wheelers. This is in agreement with the findings from Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] dealing with unpleasantness from a selection of urban road vehicle pass-by noises equalized in dB(A). This legitimizes the characterization of the 33 pass-by noises and the construction of an indicator via multiple regression.

Discussion focused on Experiment 1

Congruent with the results of the semantic differential test (cf. Chapter 3), indices accounting for the spectral and temporal features of the pass-by noises are significantly correlated with mean annoyance responses. The sharpness index did not correlate significantly with the mean annoyance responses. This is in line with findings from Paviotti and Vogiatzis [START_REF] Paviotti | On the outdoor annoyance from scooter and motorbike noise in the urban environment[END_REF] and Kaczmarek and Preis [START_REF] Kaczmarek | Annoyance of time-varying road-traffic noise[END_REF]. The characterization of annoyance using the TETC index, however, suggests that the high-frequency content plays an important role as the index correlated strongly with the mean annoyance responses. This finding is in agreement with the findings from Chapter 3 obtained through participants' verbalizations and the results of the correlation analysis between the median values of the "annoying" scale and the values of the TETC index.

Among the indices taking into account the temporal features linked to the amplitude modulations of the pass-by noises, the modulation indices m sputt,10 and m nas,10 correlated significantly with the mean annoyance ratings. The correlation coefficients of both indices were greater compared to the roughness and fluctuation strength indices. Analysis of the relationship between mean annoyance ratings and the values of the indices revealed that both fluctuation strength and roughness indices produce clusters of data points representing the road vehicle pass-by noises rather than data points spread along the regression line. Furthermore, the pass-by noises dau_5 and dao_2 known to evoke the two different modulation-related sensations "sputtering" and "nasal" (cf. Chapter 3) respectively, are calculated as similarly "rough". This is interesting as both the "sputtering" and "nasal" indices clearly differentiate these noises in terms of their modulation characteristics. These results could be related to findings from Paviotti and Vogiatzis [START_REF] Paviotti | On the outdoor annoyance from scooter and motorbike noise in the urban environment[END_REF]. They presumed that temporal features of the noises could play a role but the roughness index only explained a small portion of the variance in their experiment. The proposed indices in this work m sputt,10 and m nas,10 correlate well with mean annoyance ratings and their relationships are meaningful.

In order to assess the combination of the proposed indices and to compare the performance of the proposed "sputtering" and "nasal" indices with regard to classical psychoacoustic modulation indices, a linear multiple regression analysis between the relevant indices characterizing spectral and modulation-related features of the pass-by noises was carried out. The results show that the regression model including the "sputtering" index, the "nasal" index and the TETC index yielded the best results in terms of the adjusted R 2 value compared to the combination of psychoacoustic modulation indices and the TETC index. This is explained by the fact that the "sputtering" and "nasal" indices are independent (no significant correlation: r = 0.17; p = 0.36) and that the indices F mean and R max inserted into the regression model are significantly correlated (r = 0.52; p < 0.01) whereas, according Fastl and Zwicker [START_REF] Fastl | Psychoacoustics -Facts and Models[END_REF], they are supposed to characterize different modulation-related sensations. This point emphasizes the potential of the two proposed indices to account for different modulation sensations evoked by urban road vehicle pass-by noises that also influence noise annoyance responses.

Discussion focused on Experiment 2

The sound pressure level differences applied to the pass-by noises according to the vehicle type and driving condition had a large influence on the annoyance responses as was expected. Intensity was well characterized using mean loudness. Based on partial correlations controlling for mean loudness, the importance of the indices m sputt,10 and m nas,10 and TETC was demonstrated.

The assessment of the combination of the proposed indices via multiple linear regression analysis showed that an indicator comprising the modulation-related indices m sputt,10 , m nas,10 , mean loudness N mean , and the TETC index yielded a better model fit compared to an indicator comprising N mean , F 10 and TETC.

The "sputtering" index, as shown in the semantic differential test, accounts for the "sputtering" characteristic of the pass-by noises but does not take into account sound pressure level or loudness differences. In contrast, the F 10 and R 95 indices are loudness-dependent and consequently the combination of the proposed indices used in Model 1 enabled a more adequate calculation of annoyance than Model 2.

Conclusions

The two experiments presented in this chapter were set out with the aim of assessing the characterization of annoyance due to urban road vehicle pass-by noises using the proposed indices m sputt,10 and m nas,10 . The results obtained in both experiments showed that an annoyance indicator including the two proposed indices resulted in an increased model fit in relation to the models comprising the classical psychoacoustic indices. It would be interesting to test the noise annoyance indicator i) when different urban road traffic compositions are considered rather than urban road single-vehicle pass-by noises and ii) when urban road traffic is heard in combination with another source rather than in isolation. In the following chapters the research focuses on the assessment of annoyance due to urban road traffic noise combined with tramway noise.

Chapter 5

Perceptual phenomena involved in annoyance due to combined urban road traffic and tramway noises

Before testing the proposed indicator when urban road traffic noise is heard in presence of tramway noise, the objective is first to highlight and understand perceptual phenomena involved in annoyance due to urban road traffic noise combined with tramway noise. For this purpose, this chapter describes the methodology and results of three experiments. The first experiment is conducted in an imaginary context. Experiment B and C are carried out in simulated environments employing different procedures, apparatus and stimuli.

Introduction

The previous chapters allowed for the identification and characterization of annoyancerelevant sensations evoked by urban road single-vehicle pass-by noises and the proposal of an annoyance indicator. Noise annoyance due to transportation noises is commonly assessed by considering single noise exposure situations. In urban agglomerations, however, road traffic noise is usually not heard in isolation but in the presence of other noises which may interact with each other in terms of annoyance. The understanding of human responses resulting from combined noise sources is crucial for the application of adequate noise control strategies. In the following, the case of combined noise exposure due to different transportation noises is considered, more precisely, the combination of road traffic noise with tramway noise. Tramways are a popular alternative to common means of public transport, such as buses, as they are considered green and sustainable.

In order to investigate noise annoyance due to combined urban road traffic and tramway traffic, three experiments were conducted in laboratory conditions. Experiment A (Exp. A) was carried out in an imaginary context and Experiment B (Exp. B) and C (Exp. C) in a simulated context differing in procedures, apparatus and stimuli. The procedure adopted in Experiment A allows to test different acoustical situations of the combined noises with differences in vehicle types and traffic density regarding both road traffic and tramway traffic. The experimental settings of both Experiment B and C are close to real contexts of residents at home. These procedures, however, require the exposure of the participants to longer durations of stimuli. Consequently, less combined noise situations were studied in contrast to Experiment A. These three experiments involving different numbers and durations of stimuli allow the analyzing of perceptual phenomena involved in annoyance evoked by combined road traffic and tramway noises.

Experiment A

Experimental methodology

Recording of noises

The urban road single-vehicle pass-by noises employed in Exp. A to construct the noise sequences stem from recordings carried out in-situ by Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] in Lyon (France) and its suburbs (cf. Section 4.2.2 for more details).

The tramway recordings stem from Trollé et al. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF]. The tramway pass-by noises in in-curve operating configurations used in Exp. A and B were recorded at a distance of 7.50 m from the nearest tramway track.

Both urban road vehicle pass-bys and tramway pass-bys were recorded using the ORTF technique (devised at the former French Broadcasting Corporation -Two Schoeps MSTC 64 microphones with Schoeps BBG windfields) in accordance with French Standards NF S31-010 [START_REF]Acoustique -Caractérisation et mesurage des bruits de l'environnement[END_REF] and NF S31-085 [START_REF]Caractérisation et mesurage du bruit dû au trafic routier[END_REF]. Several researchers highlighted the advantages of this recording technique (e.g. [START_REF] Lavandier | The contribution of sound source characteristics in the assessment of urban soundscapes[END_REF]) for stereophonic sound reproduction in laboratory as it is known for its good representation, readability, plausibility and overall reproduction quality of fixed and moving noise sources [START_REF] Guastavino | Spatial audio quality evaluation: comparing transaural, ambisonics and stereo[END_REF]. The different urban road traffic and tramway noises were combined a posteriori using audio editing software.

An urban background noise was recorded early in the morning by Trollé et al. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF] without distinguishable noise events. This background noise was mixed to each combined noise sequence in Experiment A.

Stimuli

The stimuli for combined road traffic and tramway noises were constructed according to recommendations given by Berglund and Nilsson [START_REF] Berglund | Empirical issues concerning annoyance models for combined community noises[END_REF]. They proposed to study all possible combinations of five different sound pressure levels for every noise studied in combination. In this experiment, all combinations resulting from five different A-weighted sound pressure levels (denoted by SPL) for road traffic noises and five different SPL for tramway noises were studied.

As a basis for the construction of the road traffic noises, urban road vehicle pass-by noises employed in the previous chapter were used (cf. Table 4.1, Section 4.2.2). Thus, pass-by noises, such as noises of light vehicles, heavy vehicles, buses and powered-two-wheelers in different driving conditions (acceleration, deceleration and constant speed), were used. Each of these pass-by noises exhibits level differences depending on the vehicle type and driving condition corresponding to level differences measured in situ (cf. The objective was to create representative urban road traffic scenarios considering peak and off-peak hours in a city. Five road traffic noise sequences (RT1-RT5) with different traffic compositions, traffic densities and corresponding sound pressure levels were constructed. The duration of each road traffic noise sequence was 3 minutes.

To create a realistic urban road traffic with, for instance, traffic light scenarios (deceleration of a vehicle in front of a traffic light, followed by an acceleration) only certain pass-by noises could be combined due to each pass-by noises' specific sound characteristics. The pass-by noises employed in this experiment and their corresponding number of occurrences per noise sequence are presented in Appendix B.1, Table B.1. To introduce more variation within a sequence, the direction of the pass-by noises was varied (from left to right and vice versa without overlaps between the crossing vehicle pass-bys). The A-weighted equivalent sound pressure level (SPL) of these sequences increased according to the number of pass-bys per sequence and the traffic composition. It ranged from 43 dB(A) to 53 dB(A). Sequences representing off-peak hour road traffic comprised 17 pass-by noises and peak hour road traffic was represented by a total of 34 pass-by noises per sequence. The composition of the road traffic with these corresponding numbers of pass-bys per sequence is shown in Table 5.1.

The first two road traffic noises RT1 [START_REF] Guski | The concept of noise annoyance: How international experts see it[END_REF] and RT2(46) consisted of light vehicles in peak hour (34 pass-bys) and off-peak hour (17 pass-bys) scenarios. In order to study the influence of different traffic compositions on noise annoyance, RT3 [START_REF]Acoustics -Assessment of noise annoyance by means of social and socio-acoustic surveys[END_REF] and RT4 [START_REF] Kaczmarek | Annoyance of time-varying road-traffic noise[END_REF] were considered representing off-peak and peak hour road traffic scenarios, respectively, including varying numbers of powered-two-wheelers, light vehicles, heavy vehicles and buses. The structure of these road traffic sequences was adapted from recent traffic count data obtained for cities in the Paris region [START_REF] Monchatre | Étude du trafic des deux-roues motorisés en Ile-de-France -Comptages sur la saison juin et septembre, et comparaison avec la saison mars à mai[END_REF]. The last road traffic sequence RT5 [START_REF] Kaku | Predicting overall reaction to multiple noise sources[END_REF] represented peak hour traffic with Table 5.1: The road traffic noise sequences and their corresponding SPLs (L Aeq ), traffic densities, number of pass-bys and traffic compositions considering peak and off-peak hours of road traffic in a city; LV: Light vehicles, PTW: powered-twowheelers, HV: heavy vehicles, B: buses. an increased number of powered-two-wheelers in order to account for different percentages of powered-two-wheelers as observed by Monchatre et al. [74] between off-peak hours and peak hours in French cities.

Sequence

The tramway pass-by noises presented in this experiment were different tramways in in-curve operating configurations which represent one of the worst exposure situations for residents living in the vicinity of tramway tracks. As for the road traffic sequences, different scenarios of tramway traffic in a French city (peak, off-peak hours) were considered. Peak hour tramway traffic sequences were represented with two tramway pass-bys per sequence and off-peak hour tramway traffic was represented with one single tramway pass-by. To create a maximal variety of noises, the three tramway pass-by noises (T1-T3) were selected based on the SPL and the variation of an index reflecting the piercing character of squeal noise due to tonal components in high frequencies (TETC) (cf. Trollé et al. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF]). All tramway pass-by noises used for constructing the tramway sequences contain squeal noise to a certain degree. Whereas the squeal noise of T1 is only just audible, it is moderate for T2 and the strongest for the tramway pass-by noise T3. The composition of the tramway traffic sequences is shown in Table 5.2.

Table 5.2:

The tramway noise sequences and their corresponding SPLs (L Aeq ), traffic densities and number of pass-bys considering peak and off-peak hours of tramway traffic in a city. In a traffic situation where a street is adjacent to a tramway track, it is likely that tramway and road vehicle pass-by noises are heard simultaneously even at off-peak hour road traffic. To keep the same overlap between road traffic and tramway pass-by noises across the different road traffic densities studied, each tramway pass-by was simultaneously presented with two light vehicles passing by at constant speed. These light vehicles were judged as the least annoying urban road vehicle pass-by noises in Exp. 2 (cf. Section 4.4.3). It is important to note that no other road traffic noises superimposed the tramway noises. As various acoustical variables have already been introduced to this experiment (different vehicles for road and tramway traffic, different driving conditions for road vehicles, different urban traffic compositions, different traffic densities), the overlap of pass-by noises was kept constant across the studied sequences in order to control the acoustical variables and to limit the duration of the experiment. The auditory spectrograms of the tramway pass-bys used in the sequences T1, T2 and T3 are provided in Appendix A.4.

Sequence

To create a realistic sound reproduction in a quiet room, urban background noise recorded by Trollé et al. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF] was equalized to 29 dB(A) and added to the sequences. Thus, stimuli (5 road traffic noises x 5 tramway noises) were created by respecting the matrix recommended by Berglund and Nilsson [START_REF] Berglund | Empirical issues concerning annoyance models for combined community noises[END_REF] for investigating phenomena involved in combined noise exposure. In the following, combinations of road traffic and tramway traffic noises are denoted by RT2( 46)+T(X) for X = 46 to 53 dB(A).

Participants

Thirty-eight participants performed this experiment (19 males, 19 females; mean age = 31 years; standard deviation = 11 years). They all declared normal hearing abilities and were paid for their participation.

Apparatus

The experiment took place in a quiet room with a background noise measured at 19 dB(A). The stimuli were reproduced employing a 2.1 audio reproduction system consisting of two active loudspeakers (Dynaudio Acoustics BM5A), one active subwoofer (Dynaudio 115 Acoustics BM9S) and a high quality PC sound card (LynxTwo studio interface). The participants sat in front of the two loudspeakers and their respective positions complying with Bech and Zacharov's [START_REF] Bech | Perceptual audio evaluation. Theory, method and application[END_REF] recommendations: the center of the listeners' interaural-axis and the loudspeakers formed an equilateral triangle. The loudspeakers were placed at a height of 1.20 m from the floor and the subwoofer was placed on the floor between the loudspeakers. The user interface was programmed using MATLAB©.

Procedure

Prior to the start of the experiment, the participants were told that they would listen to environmental sound sequences comprising urban background noise, road traffic noise and tramway noise. The main experimental procedure adopted in this laboratory experiment is based on Berglund et al. [START_REF] Berglund | Scaling loudness, noisiness and annoyance of aircraft noise[END_REF] who defined noise annoyance as "the nuisance aspect of the noise experienced in an imaginary situation". Thus, participants were asked to imagine themselves "relaxing at home while reading". This imaginary context has also been used in other studies (e.g. [START_REF] Kim | Noise-induced annoyance from transportation noise: Short-term responses to a single noise source in a laboratory[END_REF]). Newspapers and magazines were provided.

The participants were asked to first rate the annoyance due to road traffic noise, then they were asked to rate the annoyance due to tramway noise and finally to rate annoyance due to the combination of the noises. For each stimulus, a reminder of the imaginary situation was presented to the participants: "When you imagine yourself at home in the presence of this environmental sound sequence, (...)" which was followed by the respective annoyance questions:

• "(...) did the road traffic noise annoy you?" • "(...) did the tramway noise annoy you?" • "(...) did the global noise due to road traffic and due to tramway noise annoy you?"

The response scales used to collect the annoyance ratings were derived from the recommendations given in Standard ISO/TS 15666 [START_REF]Acoustics -Assessment of noise annoyance by means of social and socio-acoustic surveys[END_REF]. The participants were asked to move a slider along a continuous scale ranging from "0" to "10", with 11 evenly spaced numerical labels and two verbal labels at both ends ("not at all annoying" and "extremely annoying"). Verbal labels were chosen in order to facilitate the use of the scale.

The stimuli were played back in random order. At the end of the test each participant conducted a verbalization task relative to the sequences heard (cf. Appendix B.2). After this, they filled out a questionnaire to gather data with regard to non-acoustical factors (cf. Appendix B.3). The experiment lasted approximately 90 minutes.

Results

Analysis of variance

In the following, annoyance caused by the combination of both road traffic and tramway noises will be referred to as "total annoyance" and annoyance caused by one noise presented within the combination as "partial annoyance". Through the construction of the stimuli, two factors have been introduced: The factor "RT" corresponding to the road traffic noises and the factor "TT" corresponding to the tramway traffic noises. The effects of these factors on the partial annoyance due to road traffic noise, partial annoyance due to tramway traffic noise and total annoyance will be studied using a two-factorial analysis of variance with repeated measures (RM ANOVA) with five levels per factor.

Partial annoyance due to urban road traffic noise

The two main factors RT and TT have a significant effect on road traffic partial annoyance (respectively [F(2.54,93.08) = 466.08; p < 0.001] and [F(4,148) = 3.05; p < 0.05]). The proportion of variance (η 2 ) explained by the factor RT is high, i.e. 65 %, and by the factor TT is very small, i.e. 1 %. The interaction between the factors RTxTT has no impact on the road traffic partial annoyances [F(8.26,305.62) = 1.65; p = 0.11]. Figure 5.1(a) depicts the mean measured partial annoyance1 due to road traffic noise as a function of the road traffic noises.

RT1(43)

RT2( 46) RT3( 48) RT4( 52) RT5(53) 0 53) were judged to be more annoying than RT1(43), RT2 [START_REF] Howell | Statistical Methods for Psychology[END_REF] and RT3 [START_REF]Acoustics -Assessment of noise annoyance by means of social and socio-acoustic surveys[END_REF]. A post-hoc analysis (Tukey's HSD test) revealed that the road traffic partial annoyance responses due to these noises are significantly different from the partial annoyance responses due to other road traffic noises. At the same traffic density, the road traffic noises RT4(52) and RT5(53) comprising powered-two-wheelers, heavy vehicles and buses in addition to light vehicles were rated more annoying than road traffic noise RT2 [START_REF] Howell | Statistical Methods for Psychology[END_REF] comprising only light vehicles. Despite the overlapping error bars of RT2 [START_REF] Howell | Statistical Methods for Psychology[END_REF] and RT3(48) (cf. Figure 5.1(a)), a post hoc test revealed that there is a small but significant difference between their road traffic partial annoyance responses. This is interesting as RT3(48) comprises particular annoying urban road vehicles, such as powered-two-wheelers, but in total only half the amount of vehicle pass-bys as RT2 [START_REF] Howell | Statistical Methods for Psychology[END_REF] which is only composed of light vehicle pass-by noises. This indicates that the traffic composition may play an important role in annoyance due to road traffic noise. The road traffic noises RT4(52) and RT5(53) were rated similarly annoying and are not significantly different. This suggests that an increase in the number of powered-two-wheelers (from 12 % in RT4(52) to 20 % in RT5( 53)) for high traffic densities does not necessarily lead to an increase in annoyance. noise as a function of the tramway traffic noises. Road traffic partial annoyance appears slightly smaller when heard in presence of T3 [START_REF] Job | Community response to noise: a review of factors influencing the relationship between noise exposure and reaction[END_REF] relative to the presence of other tramway noises. Post-hoc analysis confirmed a significant difference between road traffic partial annoyance rating when road traffic is heard in presence of T3 [START_REF] Job | Community response to noise: a review of factors influencing the relationship between noise exposure and reaction[END_REF] and 2xT3 [START_REF] Kaku | Predicting overall reaction to multiple noise sources[END_REF].

Partial annoyance due to tramway traffic noise The factors TT and RT have a significant effect on the mean measured partial annoyance due to tramway noises (respectively [F(2.66,98.03) = 53.31; p < 0.001] and [F(2.66,98.03) = 53.31; p < 0.001]). The proportion of variance explained by the factor TT is high, i.e. 82 %. The corresponding proportion of variance explained by the factor RT is smaller, i.e. 8 %. Hence, the road traffic noises influenced partial annoyance due to tramway noise and the tramway noises influenced partial annoyance due to road traffic noise. The ANOVA showed that road traffic noises have a greater effect on the tramway partial annoyance, than the tramway noises have on road traffic partial annoyance. A post-hoc test revealed that the tramway traffic noise T3(50) exhibiting the strongest squeal noise was judged more annoying than T1 [START_REF] Howell | Statistical Methods for Psychology[END_REF] with little audible squeal noise. T2 [START_REF]Acoustics -Assessment of noise annoyance by means of social and socio-acoustic surveys[END_REF] with moderate squeal noise was rated similarly annoying as T1 [START_REF] Howell | Statistical Methods for Psychology[END_REF] and T3 [START_REF] Job | Community response to noise: a review of factors influencing the relationship between noise exposure and reaction[END_REF]. The different partial annoyance ratings for the tramways may be influenced by the presence of road traffic noise. It can clearly be seen that peak hour tramway traffic was rated more annoying than off-peak hour tramway traffic. This also confirmed by a post-hoc test. As observed for road traffic partial annoyance, the interaction between the factors TTxRT has no impact on tramway traffic partial annoyance [F(8.07,298.65) = 1.92; p = 0.06]. Figure 5.2 illustrates the mean measured partial annoyance due to the tramway noise as a function of the tramway noises (a) and as a function of the road traffic noises (b) presented in combination. From Figure 5.2(a) it is apparent that partial annoyance due to peak hour tramway traffic noises is significantly higher than for off-peak hour tramway traffic noises. The influence of the road traffic noises on partial annoyance due to tramway noise can also be seen (Figure 5.2(b)): For the more annoying road traffic noises RT4 [START_REF] Kaczmarek | Annoyance of time-varying road-traffic noise[END_REF] and RT5(53) (cf. Figure 5.1) with traffic compositions comprising heavy vehicles, powered-two-wheelers and buses, the partial annoyance due to tramway noises is greater compared to the partial annoyance of tramway traffic presented in combination with off-peak hour road traffic noises (RT1(43), RT3 [START_REF]Acoustics -Assessment of noise annoyance by means of social and socio-acoustic surveys[END_REF]) and peak hour road traffic comprising only light vehicles (RT2( 46)). Post-hoc analysis revealed that the partial annoyance ratings for RT1 [START_REF] Guski | The concept of noise annoyance: How international experts see it[END_REF] and RT2 [START_REF] Howell | Statistical Methods for Psychology[END_REF] are significantly different from RT4 [START_REF] Kaczmarek | Annoyance of time-varying road-traffic noise[END_REF] and RT5 [START_REF] Kaku | Predicting overall reaction to multiple noise sources[END_REF].

Total annoyance The total annoyance responses collected in this experiment were analyzed using a two-factorial analysis of variance with repeated measures and five levels per factor.

The factors RT and TT influence total annoyance ratings (respectively [F(2.56,94.82) = 103.43; p < 0.001] and [F(4,148) = 19.86; p < 0.001]). The proportion of variance explained by the factor RT is high, i.e. 88 %, and by the factor TT is smaller, i.e. 9 %, indicating that the road traffic sequences mostly influenced total annoyance responses. The interaction between the factors RTxTT is small but significant [F(8.02,296.71) = 2.1; p < 0.05]. It accounts for only 4 % of the variance. 53) with tramway traffic noises is higher. Post-hoc analysis showed that these noises are significantly more annoying than RT1(43), RT2 [START_REF] Howell | Statistical Methods for Psychology[END_REF] and RT3 [START_REF]Acoustics -Assessment of noise annoyance by means of social and socio-acoustic surveys[END_REF]. Furthermore, from Figure 5.3(b) it can be seen that total annoyance is higher in presence of peak hour tramway traffic exhibiting squeal noise 2xT3 [START_REF] Kaku | Predicting overall reaction to multiple noise sources[END_REF]. This observation is confirmed by a post-hoc analysis yielding significantly different annoyance ratings for 2xT3(53) compared to annoyance ratings obtained for combinations with the other tramway noises. It is also interesting to note that total annoyance due to off-peak hour road traffic noises (RT1(43), RT3( 48 The interaction between the factors RTxTT is also shown in Figure 5.4. The small interaction is due to the fact that 2xT3(53) heard in combination with peak hour road traffic noises (RT2(46), RT4( 52),(RT5(53)) leads generally to higher total annoyance compared to other combinations of tramway traffic and road traffic noises except when 2xT3(53) was combined with off-peak hour road traffic noises (RT1(43), RT3(48)).

Effects regarding partial and total annoyance

To assess the relationships between partial and total annoyances, mean measured total and partial annoyance responses are shown based on the representation proposed by Vos [START_REF] Vos | Annoyance caused by simultaneous impulse, road-traffic, and aircraft sounds: a quantitative model[END_REF]. This representation allows for the analysis of the partial and total annoyances of the combined noises studied. 52)+T(X), as a function of the SPL of tramway noises; : Mean measured partial annoyance due to tramway noises (A part tramway). The linear regression line between A part tramway and the tramway SPL is calculated (A calc tramway); : Mean measured partial annoyance due to peak hour road traffic RT4(52) (A part RT4(52)) 2 .

: Standard error (SE) of A part RT4 [START_REF] Kaczmarek | Annoyance of time-varying road-traffic noise[END_REF]. The errorbars represent the standard error of A tot and A part tramway.

From Figures 5.5(a) and (b) it can be seen that total annoyance mainly approximates the highest partial annoyance which will be, in the following, referred to as the strongest component (cf. [START_REF] Berglund | Empirical issues concerning annoyance models for combined community noises[END_REF][START_REF] Botteldooren | Fuzzy models for accumulation of reported community noise annoyance from combined sources[END_REF]). This suggests that the strongest component effect prevails for annoyance due to RT2(46)+T(X) and RT4(52)+T(X). However, for the combinations RT2(46)+2xT1(49) and RT4(52)+2xT3(53), total annoyance was judged significantly greater than their respective strongest components (confirmed by paired t-tests [START_REF] Howell | Statistical Methods for Psychology[END_REF]) indicating the presence of the synergistic effect. For other combinations of road traffic and tramway traffic noises, such as RT3(48)+T2(48), RT3(48)+2xT1 [START_REF] Izumi | Annoyance due to mixed source noises -a laboratory study and field survey on the annoyance of road traffic and railway noise[END_REF] and RT3(48)+T3(50), total annoyance was also found to be greater and significantly different than the corresponding strongest components. Hence, the synergistic effect prevails in 20 % of the 25 combinations where total annoyance was superior to the strongest component.

Regarding the partial annoyance responses, Figures 5.5(a) and (b) show that the partial annoyance due to road traffic noises RT2 [START_REF] Howell | Statistical Methods for Psychology[END_REF] and RT4 [START_REF] Kaczmarek | Annoyance of time-varying road-traffic noise[END_REF] are greater than tramway traffic partial annoyances with the exception of the combination RT2 [START_REF] Howell | Statistical Methods for Psychology[END_REF]+2xT3 [START_REF] Kaku | Predicting overall reaction to multiple noise sources[END_REF]. Even though tramway noises represent dominant sources (T2(48), T3(49), 2xT1(50), 2xT3( 53)) with a level superior to the road traffic noise RT2 [START_REF] Howell | Statistical Methods for Psychology[END_REF], the road traffic noise represents in most combinations the strongest component. These observations are consistent for RT4(52)+T(X) despite for RT4(52)+2xT3 [START_REF] Kaku | Predicting overall reaction to multiple noise sources[END_REF] for which the synergistic effect could be observed. This indicates an asymmetry between partial annoyance due to tramway and road traffic noises. In combination tramway traffic noises must be more dominant in terms of the SPL to become as annoying as the road traffic noise. For the combinations shown in Figure 5.5, the tramway noise requires an increased SPL of 6 dB(A) to become as annoying as RT2 [START_REF] Howell | Statistical Methods for Psychology[END_REF] and an increased SPL of 5 dB(A) to become as annoying as RT4 [START_REF] Kaczmarek | Annoyance of time-varying road-traffic noise[END_REF]. Considering the other combinations this asymmetry ranged from 3 dB(A) to 6 dB(A) depending on the composition of the road traffic and tramway traffic noises.

Analysis of the verbalization task

The analysis of the verbalization task focuses on the descriptive terms or phrases given by the participants when they were asked after the experiment. To recall, the participants were asked two questions in the interview which followed after the experiment: What do you think about the tramway noises? "Qu'avez-vous pensé du bruit du tramway?" and Could you tell me, what did you think of the road traffic noises? "Pouvez-vous dire ce que vous avez pensé du bruit de la circulation routière?".

Evidence was found which supports the observations of the strongest component effect and the synergistic effect. Analysis of the number of occurrences of terms and phrases given by the participants shows that 18 out of 22 mentions support the strongest component effect. For instance, five participants mentioned "road traffic noise is more annoying than the tramway noise": "bruit routier est plus gênant que le bruit de tramway". However, 2 out 22 descriptions indicate that the tramway noise may also contribute to overall annoyance (e.g. "the road traffic noises could be as disturbing as the tramway noises": "les bruits routiers peuvent être tout aussi dérangeants que les bruits de tramways"). Furthermore, participants provided descriptions which are in line with the observations regarding the synergistic effect. Out of the 22 descriptions, three phrases highlighted this effect (e.g. "the tramway noise in addition to powered-twowheelers amplifies the sound and this is more disturbing": "le bruit de tramway plus un deux-roues, ça amplifie encore le son et c'est plus perturbant").

Moreover, participants highlighted the importance of the number of events in noise annoyance assessment. High annoyance ratings of road traffic partial annoyance noise were mentioned in line with a high number of events (e.g. "road traffic noise is more annoying because there is more traffic": "plus gêné parce qu'il y a plus de trafic"). Also, the density of the road traffic was highlighted compared to tramway traffic (e.g. "more frequently": "plus fréquent", "more dense": "plus dense"). It was also mentioned that there were less tramway pass-by noises (e.g. "less frequently": "moins souvent").

Experiment B

Experiment B was a carried out in an experimental living room employing similar stimuli as in Exp. A. This section describes the experimental procedure used in Experiment B and the results obtained.

Experiment methodology

Recordings

The same recordings were employed for the construction of the stimuli as outlined in Section 5.2.1.1.

Stimuli

The stimuli constructed for Experiment A were used as a basis to construct the stimuli for Experiment B. Thus, the same urban road traffic and tramway traffic pass-by noises were employed. As this experiment is carried out in a simulated environment, it was important to expose the participants to longer durations of environmental noises. Thus, stimuli duration was increased to 6 min. This is in line with other studies (e.g. [START_REF] Coensel | Experimental investigation of noise annoyance caused by highspeed trains[END_REF]). To keep the total duration of the experiment within acceptable limits for the participants, the combined stimuli were constructed based on four road traffic noises and four tramway traffic noises. Along with the increase of the stimuli duration was the increase of pass-bys per sequence and its sound pressure level. Four road traffic sequences employed in Exp. A were selected which were then adapted to longer durations keeping the same traffic compositions relative to the total number of pass-bys. Thus, off-peak hour road traffic noises were simulated with 34 pass-bys per sequence and peak hour road traffic with 68 pass-bys per sequence. Table 5.3 summarizes the composition of the road traffic noises employed in this experiment.

Table 5.3: The road traffic noise sequences and their corresponding SPLs (L Aeq ), traffic densities, number of pass-bys and traffic compositions considering peak and off-peak hours of road traffic in a city; LV: Light vehicles, PTW: powered-twowheelers, HV: heavy vehicles, B: buses. Peak hour tramway traffic was represented with four tramway pass-bys per sequence and off-peak hours tramway traffic with two tramway pass-bys. As opposed to Experiment A, in this experiment only one peak hour tramway traffic was presented to the participants. Table 5.4 illustrates the tramway traffic noises.

Sequence

Table 5.4: The tramway noise sequences and their corresponding SPLs (L Aeq ), traffic densities and number of pass-bys considering peak and off-peak hours of tramway traffic in a city. In contrast to Experiment A, no artificial background noise was added to the sequences. The window of the experimental living room was partially open and the local urban background noise was measured at 39 dB(A) in the living room. Thus, a total of 16 stimuli (4 road traffic noises x 4 tramway noises) are used in this experiment.

Sequence

Apparatus

The listening experiment is carried out in an experimental cottage on the campus of the ENTPE simulating a living room-like setting. This experimental living room is furnished to resemble a standard living room with a couch, two chairs, a coffee table, a shelf and plants. In order to create realistic sound reproduction, the loudspeakers were placed outside the experimental living room and the window was slightly opened (approx. 15 cm). The view from the inside of the living room through the window is composed of a garden 10 m in depth with trees and a high hedge separating the garden from a small pedestrian street. Parallel to the hedge and approximately 200 m away was a street connecting the residential area. Figure 5.6 shows a plan of the experimental set-up including the experimental living room, the control room and the set-up of the loudspeaker.

For participants seated on the chairs and the couch, the loudspeakers were not visible due to plants growing just in front of the façade of the cottage (cf. Figure 5.7). The experimenter controlled the experiment from the control room using a PC equipped with a high quality sound card (LynxTwo studio interface). The stimuli were stereophonically reproduced employing a 2.1 audio reproduction system consisting of two active loudspeakers (Dynaudio Acoustics BM5A) and one active subwoofer (Dynaudio Acoustics BM9S ). The two loudspeakers were placed at a height of 1.20 m in front of the façade and the subwoofer was placed on ground between the two loudspeaker. This loudspeaker set-up was inspired by Bech and Zacharov's [START_REF] Bech | Perceptual audio evaluation. Theory, method and application[END_REF] recommendations: The center of the coffee table and the loudspeakers form a equilateral triangle. The loudspeakers were placed 5 m from each other and 3 m from the façade. Thus, the stimuli were recorded at the position of the coffee

Participants

Experiment B was performed by 34 participants (16 female, 18 male; mean age = 33.2 years; standard deviation = 13 years). Up to five participants conducted the experiment simultaneously. All participants declared normal hearing abilities and were paid for their participation.

Procedure

Prior to commencing the experiment, the participants were introduced to the experiment and asked to imagine themselves at home in their living room with an open window relaxing and performing a reading activity. Newspapers and magazines were provided. The participants were told that they would be in the presence of environmental sound sequences comprising road traffic noise and tramway noise. Furthermore, they were advised not to talk during the experiment and not to close the window.

Printed rating scales were handed out to each participant after the playback of each stimulus. The order of rating scales is consistent with Experiment A: First, participants were asked to rate partial annoyance due to road traffic noise, the partial annoyance due to tramway noise and finally total annoyance due to the combination of road traffic and tramway noise. A reminder of the imaginary situation was provided on the rating sheets: "When you imagine yourself at home in the presence of this environmental sound sequence, (...)" which was followed by the respective annoyance questions:

• "(...) did the road traffic noise annoy you?" • "(...) did the tramway noise annoy you?" • "(...) did the global noise due to road traffic and due to tramway noise annoy you?"

The response scale for partial annoyances due to road traffic and tramway traffic and for total annoyance was derived from the recommendations given in Standard ISO/TS 15666 [START_REF]Acoustics -Assessment of noise annoyance by means of social and socio-acoustic surveys[END_REF]. The participants were asked to give their ratings along a continuous scale ranging from "0" to "10", with 11 evenly spaced numerical labels and two verbal labels at both ends ("not at all annoyed" and "extremely annoyed"). At the end of the experiment, the participants filled out a questionnaire with a verbalization task (the same questionnaire presented in Exp. A was used, cf. Appendix B.3). In presence of the experimentator, the participants independently carried out the verbalization task on paper (cf. Appendix B.2). The experiment lasted approximately 2 hours.

Results

Analysis of variance

In order to study the effects of the factors RT and TT on partial and total annoyance, a two-factorial RM ANOVA with four levels was carried out. As can be seen in Figure 5.8, peak hour road traffic RT4 [START_REF] Kuwano | Subjective evaluation of car door sound[END_REF] was rated as more annoying compared to the other road traffic noises. Interestingly, the different numbers of vehicle pass-by noises in RT2 [START_REF] Job | Factors contributing to reported reaction to combined noise sources[END_REF] and RT3 [START_REF] Kaku | Predicting overall reaction to multiple noise sources[END_REF] did not cause different annoyance responses (not significant difference, confirmed by Tukey's HSD test). This suggests that different road traffic compositions influence road traffic partial annoyance. Complex road traffic compositions comprising powered-two-wheelers, heavy vehicles and buses such as RT3(53) may cause higher road traffic partial annoyance even for low traffic density than road traffic comprising entirely light vehicle pass-bys (RT2(51)) with high traffic density. Hence, off-peak hour road traffic with a small number of pass-bys but including powered-two-wheelers and heavy vehicles cause the same annoyance as peak hour road traffic with a doubled amount of light vehicle pass-bys. This is congruent with findings from Exp. 2 (cf. Section 4.4.3) in which the urban road vehicle pass-by noises were tested in single noise exposure situations. Generally, it was found that powered-two-wheelers, heavy vehicles and buses cause more extensive annoyance reactions than light vehicles. As shown in Figure 5.9(a) the peak hour tramway traffic noise 4xT3 [START_REF] Kuwano | Subjective evaluation of car door sound[END_REF] was rated as the most annoying tramway noise. This is confirmed by a post-hoc test. This finding is congruent with the results of Exp. A (cf. Section 5.2.2.1). Post-hoc analysis revealed further that off-peak hour tramway traffic 2xT2(53) exhibiting moderate squeal noise was judged significantly more annoying than 2xT1(51) with little audible squeal noise. The significant differences between the tramway partial annoyance responses observed across the experiments must be interpreted with caution as they may also become non-significant. Here, off-peak hour tramway traffic 2xT3 [START_REF] Kephalopoulos | Advances in the development of common noise assessment methods in europe: The cnossos-eu framework for strategic environmental noise mapping[END_REF] with strong squeal noise was rated similarly annoying as 2xT1 [START_REF] Job | Factors contributing to reported reaction to combined noise sources[END_REF] and 2xT2 [START_REF] Kaku | Predicting overall reaction to multiple noise sources[END_REF]. The different partial annoyance ratings for the same tramways used in Exp. A and B may be explained by the presence of road traffic noise which may have influenced the tramway traffic partial annoyance ratings.

Partial annoyance due to urban road traffic noise

Partial annoyance due to tramway traffic noise

Figure 5.9(b) shows that mean measured partial annoyance due to tramway traffic noises in combination with different road traffic noises is different according to the different road traffic noises. This effect is, however, very weak. The effect may have been stronger if more acoustical situations would have been studied as it was observed in Exp. A (cf. Section 5.2.2.1). Post-hoc analysis revealed that combinations of tramway traffic noises with RT3(53) were rated significantly less annoying than combinations with RT4 [START_REF] Kuwano | Subjective evaluation of car door sound[END_REF]. peak hour tramway traffic 4xT3(58) was judged significantly higher than all the other combinations.

Total annoyance

Effects regarding partial and total annoyance

Total and partial annoyances were analyzed based on Vos' representation [START_REF] Vos | Annoyance caused by simultaneous impulse, road-traffic, and aircraft sounds: a quantitative model[END_REF]. RT2 [START_REF] Job | Factors contributing to reported reaction to combined noise sources[END_REF] represents peak hour road traffic noise with a traffic composition consisting entirely of light vehicles. RT3 [START_REF] Kaku | Predicting overall reaction to multiple noise sources[END_REF] simulates off-peak hour road traffic with a different traffic composition comprising powered-two-wheelers, buses, heavy vehicles and light vehicles (cf. Table 5.3). For all the combinations of RT1( 48)+T(X) and RT4( 58)+T(X), total annoyance mostly approximates the strongest component suggesting the presence of the strongest component effect. This was also observed in Exp. A. However, in certain cases total annoyance due to the combination of road traffic and tramway traffic noises is inferior to the strongest component. For instance, a t-test confirmed that the total annoyance due to the combination of RT1(48)+4xT3( 58) is significantly different (inferior) to the strongest component which is tramway traffic partial annoyance for this combination. For combinations of RT4 [START_REF] Kuwano | Subjective evaluation of car door sound[END_REF] with 2xT1(51), 2xT2 [START_REF] Kaku | Predicting overall reaction to multiple noise sources[END_REF], 2xT3(55) road traffic partial annoyance was found to be higher than the total annoyance. This indicates that the combined noise source paradox occurred; meaning that ratings for total annoyance may be lower than the maximal annoyance caused by one of the combined sources (cf. Section 1.4.1.4). In 25 % of the possible combinations the paradox prevails whereas in 75 % of the cases total annoyance : Standard error (SE) of A part RT4 [START_REF] Kuwano | Subjective evaluation of car door sound[END_REF]. The errorbars represent the standard error of A tot and A part tramway.

was equal to the maximal partial annoyance.

Furthermore, an asymmetry between road traffic noise and tramway traffic noises could be observed ranging from approximately 5 dB(A) to 6 dB(A).

Analysis of the verbalization task

The analysis of the verbalization task focuses on the number of descriptive terms that relate to perceptual phenomena. It must be noted that this task was carried out on paper by each participant in presence of the experimentator: this resulted in less responses compared to Exp. A in which the interviewer noted the verbalizations given in a face-to-face interview with the participants.

Out of four descriptive terms relating to total annoyance models, three support the strongest component effect (e.g. "I felt most disturbed by the motorcycles and trucks": "les motos et camions m'ont plus dérangé", "tramway noise more acceptable than a scooter without exhaust pipe": "bruit de tramway plus acceptable qu'une mobylette sans pot d'échappement"). One descriptive term indicated that the tramway noise was more noisy than the road traffic noise ("road traffic noise is less noisy than the tramway noise": "bruit routier est moins bruyant que le bruit de tramway").

Experiment C

Introduction

Experiment C was carried out at IFSTTAR, Bron-Lyon in the LSEE (Laboratoire de Simulation et d'Évaluation et de l'Environnement), a simulated living room. This experiment has been carried out by Philipps-Bertin et al. [START_REF] Philipps-Bertin | Etude qualitative de la gêne en situation de multi-exposition au bruit du tramway et au bruit routier urbain[END_REF] and Champelovier et al. [START_REF] Philipps-Bertin | Etude qualitative de la gêne en situation de multi-exposition au bruit du tramway et au bruit routier urbain -Emission VIbratoire et Acoustique des véhicules URBAins et situations de gêne[END_REF] in the framework of the project VIA URBA. The objective of this project was to gain understanding of the mechanisms involved in the perception of tramway noise combined with road traffic noise. This section focuses on the evaluation of partial and total annoyance due to the combination of the noises which was part of this research. The objective of the analysis conducted in this PhD is to investigate the perceptual phenomena involved in this experiment and to compare them to the phenomena observed in Exp. A and Exp. B. The experiment carried out by Philipps-Bertin et al. [START_REF] Philipps-Bertin | Etude qualitative de la gêne en situation de multi-exposition au bruit du tramway et au bruit routier urbain[END_REF] is summarized hereafter from Section 5.4.2.1 to 5.4.2.5.

Experimental methodology

Recordings

Combined noise sequences were created from recordings carried out at a measurement site in Lyon (Avenue Thiers, along the "T1" tramway line). Road traffic was present on three lanes: two in south-north direction on the tramway side and third in the north-south direction. The recording system consisted of a linear microphone array with 16 microphones placed at height of 1.8 m.

Stimuli

Four combined noise sequences were created. In this study, off-peak hour tramway traffic corresponds to four tramway pass-bys per sequence and peak hour tramway traffic to 9 pass-bys per sequence. The tramway densities studied in Exp. C correspond to the densities studied in Exp. A and B. The tramway pass-by noises corresponded to pass-bys on straight tracks. When a tramway sequence is mixed with a road traffic sequence, three different tramway pass-bys were presented in each sequence by controlling the overlap between tramway pass-by and road traffic: one tramway pass-by without overlap, a second tramway pass-by combined with a small number of road vehicles and a third tramway pass-by with a higher number of road vehicles. The road traffic density was either high (R+) or low (R-) corresponding to peak and off-peak hour road traffic, respectively. For the project VIA URBA, the IFSTTAR team made the choice not to consider powered-twowheelers and trucks, thus the road traffic sequences only comprised light vehicle pass-bys. Such road traffic situations may occur in certain streets. Table 5.5 shows the combinations of tramway and road traffic noises and their corresponding SPL4 . Table 5.5: Sequences with combined road and tramway traffic noises and their corresponding SPLs (L Aeq ). The duration of each sequence was 16 minutes. In order to keep the total duration of the experiment within acceptable limits, only four noise sequences were studied in this experiment. Upon the arrival of the participants in the experimental living room and until the end of the experiment, a background noise was played back resembling an urban background noise without vehicle pass-bys (L Aeq = 35 dB(A)).

Participants

Ninety participants, 46 women and 44 men, took part in the experiment. The mean age was 42.4 years (minimum 18, maximum 65). The participants were paid for their participation.

Apparatus

The experiment was conducted at the LSEE laboratory at IFSTTAR, Bron-Lyon. The LSEE is an experimental living room simulating a living room situated on the first floor with a window which is exposed to tramway traffic and road traffic. A picture is projected on a screen placed behind a window. During sound reproduction the window was partially open. The noises were reproduced using a wavefield synthesis system which was placed behind this screen and was therefore not visible for the participants. An image is projected on the screen showing a residential garden with a high hedge blocking the view to a road.

Procedure

After reading information about the experiment, the participants were asked to sit comfortably in the experimental living room where they were instructed. Participants were told to imagine themselves living on the ground floor of a building close to a street and tramway tracks. Four sequences were played back in random order. During playback of the stimuli, the participants could read magazines which were provided. Two participants conducted the experiment simultaneously which lasted approximately 2 hours 30 minutes.

After the playback of the first stimulus, a question was asked to the participants: If you had to describe the noise of the street you have just heard, what would you say? Say everything which comes to your mind when you think about this sequence. Try to imagine yourself describing this sequence to someone who has just moved in here. "Si vous deviez décrire le bruit de la rue que vous venez d'entendre que diriez-vous ? Dites tout ce qui vous vient à l'esprit spontanément quand vous pensez à cette séquence". The aim was to obtain spontaneous descriptions of the combined noises heard. After the presentation of each noise sequence, the participants answered a questionnaire using a tablet PC.

• Participants were asked to provide a description of the sequence, a description of the tramway noise and of the road traffic noise, • Moreover, they were asked for a description of the perception of the combination of sources and a comparison of two sequences in terms of noisiness, level, presence, perception of time between two tram pass-bys and perception of quiet periods, • Finally, participants were asked to evaluate tramway traffic partial annoyance, road traffic partial annoyance and total annoyance due to the combination of the noises. The annoyance questions complied with ISO/TS 15666 [START_REF]Acoustics -Assessment of noise annoyance by means of social and socio-acoustic surveys[END_REF] and ratings collected on an eleven-point numerical scale ranging from 0 to 10. Two verbal labels were placed at both ends ("not at all annoying" and "extremely annoying").

The following analysis is based on the annoyance ratings and focuses on perceptual phenomena which may be highlighted through those annoyance ratings.

Effects regarding partial and total annoyance

As for Exp. A and Exp. B., the annoyance ratings due to the four combined noise sequences presented in Exp. C are analyzed using Vos' representation. The partial and total annoyance ratings are on ordinal level, thus median values are used for the assessment of central tendencies. Figures 5.12 and 5.13 show the median partial and total annoyance responses as a function of the SPL of the tramway noises (L p,4T , L p,9T ) for off-peak hour (R-) and peak hour (R+) road traffic, respectively. Due to the small number of stimuli, the two median partial annoyance ratings for each of the road traffic sequences are illustrated 5 . Median annoyance response Figure 5.12 illustrates total annoyance as a function of the SPL of the tramways (L p,4T , L p,9T ) for off-peak hour (R-) road traffic. As shown in Figure 5.12, the road traffic partial annoyance in presence of off-peak hour tramway traffic (A part R-(4T)) and the road traffic partial annoyance in presence of peak hour tramway traffic (A part R-(9T)) appears to be rated differently. A Wilcoxon signed-rank test (cf. [START_REF] Howell | Statistical Methods for Psychology[END_REF]) revealed however, that this difference is not significant. Regarding off-peak hour tramway traffic combined with off-peak hour road traffic, a Wilcoxon signed-rank test showed that total annoyance (A tot R-4T) was rated significantly higher than the strongest component (A part 4T (R-)) suggesting the presence of the synergistic effect. Concerning peak hour tramway traffic combined with off-peak hour road traffic, total annoyance (A tot R-9T) was rated as annoying as the tramway traffic partial annoyance (A part 9T (R-)), thus suggesting the presence of the strongest component effect. Concerning peak hour road traffic in combination with off-peak hour tramway traffic (R+4T) and peak hour tramway traffic (R+9T), total annoyance approximates the strongest component (A part R+ (4T)) suggesting the presence of the strongest component effect.

L p,4T L p,9T 1
A tot R-4T A tot R-9T A part 4T (R-) A part 9T (R-) A part R-(9T) A part R-(4T) approx. CI [A part R-(9T)] approx. CI [A part R-(4T)]
In this experiment, road traffic noise was found to cause generally higher partial annoyance responses than tramway traffic. This is in agreement with Exp. A and B. Based on the partial annoyance responses, an asymmetry between approximately 9-10 dB(A) between road traffic and tramway noises could be estimated. This estimation must be interpreted with caution as the number of combined noise sequences studied in Exp. C is very small. More reliable estimations would be obtained with a higher number of stimuli. Throughout Exp. A, B and C similar tendencies regarding partial annoyance responses could be observed. First, it could be observed that the road traffic and tramway traffic partial annoyances were influenced by each other. Generally, at high road traffic densities (corresponding to high road traffic levels for Experiment A and B), partial annoyance due to tramway noise tended to increase. For Exp. A it was found that road traffic and tramway traffic partial annoyances influenced each other with a weak effect for tramway traffic on partial annoyance due to road traffic (η 2 = 0.01). In Exp. B, tramway traffic was not found to influence road traffic partial annoyance whereas road traffic partial annoyance had a significant effect on tramway traffic partial annoyance. Furthermore, for total annoyance in Exp. A, a small interaction effect between road and tramway traffic noises could be observed (η 2 = 0.04). This effect was not observed in Exp. B, which may be due to the limited combinations of noises studied in Exp. B compared to Exp. A (16 combinations in Exp. B instead of 25 combinations in Exp. A). This difference may not allow to highlight the small effect. Öhrström et al. [START_REF] Öhrström | Annoyance due to single and combined sound exposure from railway and road traffic[END_REF] obtained similar results studying combinations of highway traffic noises and railway noises in situ. They found that partial annoyances were higher in combination with the other noise.

The same tendency observed in Exp. A and B regarding tramway traffic partial annoyance was also observed in Exp. C. Tramway traffic partial annoyance was rated higher when it was heard in presence of higher road traffic density. Following the same tendency, Champelovier et al. [START_REF] Champelovier | Perception du bruit du tramway dans des situations contrastées de bruit de trafic routier urbain[END_REF] found that the off-peak hour tramway traffic in combination with peak hour road traffic was evaluated "louder" than in combination with off-peak hour road traffic.

Trollé and Marquis-Favre [START_REF] Trollé | Indicateurs acoustiques de la gêne en situation de mono-exposition au bruit de tramway[END_REF] assessed short-term annoyance in single noise exposure situations due to a corpus of 14 tramway noises in in-curve operating configurations. They found that among the three tramway noises T1, T2 and T3 6 , the tramway noise with strong squeal noise T3 was rated most annoying, T2 moderately annoying and T1 was among the least annoying pass-by noises. They revealed in a post-hoc analysis that these stimuli were rated significantly different. In this work, partial annoyance due to the three off-peak hour tramway traffic comprising the tramway noises recorded by Trollé [START_REF] Trollé | Indicateurs acoustiques de la gêne en situation de mono-exposition au bruit de tramway[END_REF] was rated differently in Exp. A and Exp. B. In Exp. A, T3(50) exhibiting the strongest squeal noise among the tested tramway traffic noises was rated significantly more annoying than T1 [START_REF] Howell | Statistical Methods for Psychology[END_REF] with little audible squeal noise but T2 with moderate squeal noise was judged as annoying as T1 [START_REF] Howell | Statistical Methods for Psychology[END_REF] and T3. Furthermore, the tramway T3 used in Exp B. for off-peak hour tramway traffic (i.e. 2xT3 [START_REF] Kephalopoulos | Advances in the development of common noise assessment methods in europe: The cnossos-eu framework for strategic environmental noise mapping[END_REF]) was rated as annoying as the other two off-peak hour tramway traffic noises (2xT1 [START_REF] Job | Factors contributing to reported reaction to combined noise sources[END_REF], 2xT2( 53)) in Exp. B. The significant differences in annoyance ratings collected for different tramway pass-bys in single noise exposure (cf. [START_REF] Trollé | Indicateurs acoustiques de la gêne en situation de mono-exposition au bruit de tramway[END_REF]) may become non-significant when the tramway pass-bys are heard in presence of urban road traffic noise. Another explanation may lie in the activity performed during the experiments: in Exp. A and B participants performed a reading activity whereas in Trollé and Marquis-Favre [START_REF] Trollé | Indicateurs acoustiques de la gêne en situation de mono-exposition au bruit de tramway[END_REF] participants did not conduct a specific activity. Hence, participants may not have systematically noticed the squeal noise inherent in the tramway pass-by noises.

The results of Exp. A and B emphasize the importance to account for the types of urban road vehicle in noise annoyance assessment and in the construction of noise maps. Powered-two-wheelers were found to influence partial annoyance due to urban road traffic in both experiments: off-peak hour and peak hour road traffic comprising powered-two-wheelers was rated more annoying than off-peak hour and peak hour road traffic comprising only light vehicles. Furthermore, it was shown in Exp. B that a road traffic comprising different types of vehicles (light vehicles, powered-two-wheelers, heavy vehicles, buses) may cause the same partial annoyance as a road traffic comprising only light vehicle pass-by noises but with a doubled amount of pass-bys. This supports the finding of Gille and Marquis-Favre [START_REF] Gille | Étude de l'influence de l'ordre de passage successif des véhicules routiers urbains sur la gêne sonore[END_REF] who point out that noise annoyance due to road traffic noise is determined by the "worst" event within a sequence.

The results of Exp. A revealed that for peak hour road traffic an increase in the number of powered-two-wheelers from 0 to 12 % resulted in higher road traffic partial annoyance. However, a further increase in the number of powered-twowheelers from 12 % to 20 % did not involve an increase in partial annoyance due to peak hour road traffic. This outcome suggests that a certain breakpoint for annoyance as the one highlighted by Björkman [START_REF] Björkman | Community noise annoyance: Importance of noise levels and the number of noise events[END_REF] for a specific number of heavy vehicles per day in situ may also hold true for powered-two-wheelers. Björkman [START_REF] Björkman | Community noise annoyance: Importance of noise levels and the number of noise events[END_REF] demonstrated that annoyance increased until about 2000 heavy vehicles a day in urban areas. He showed that from this threshold an increase in the number of heavy vehicles did not involve a further increase in annoyance. Assuming constant traffic flow during the day, four powered-two-wheeler pass-bys in 3 minutes (RT4) correspond to 1920 powered-two-wheelers a day which is close to the breakpoint observed by Björkman. To confirm this finding, further studies should be carried out.

Partial annoyance in relation to total annoyance

In the majority of combinations road traffic noises represented the strongest component in Exp. A (64 % of the 25 studied combinations), Exp. B (56 % of the 16 studied combinations) and Exp. C (75 % of the four studied combinations). The importance of road traffic noise combined with railway noise was also shown in laboratory and field studies dealing with annoyance due to combined railway and road traffic noises [START_REF] Ronnenbaum | Synergetic effects of noise from different sources: A literature study[END_REF][START_REF] Izumi | Annoyance due to mixed source noises -a laboratory study and field survey on the annoyance of road traffic and railway noise[END_REF].

In 20 % of the combinations in Exp. A, the synergistic effect occurred. In Exp. C the synergistic effect was also observed but due to the small number of stimuli employed in the experiment conclusions must be drawn carefully. Studying combined railway and road traffic noises in laboratory conditions, Ota et al. [START_REF] Ota | The metrics of mixed traffic noise: results of simulated environments experiments[END_REF] also found synergistic effects.

In 25 % of the studied combinations in Exp. B the combined noise source paradox occurred. In literature the combined noise source paradox was found in laboratory and field studies (e.g. [START_REF] Alayrac | Total annoyance from an industrial noise source with a main spectral component combined with a background noise[END_REF][START_REF] Taylor | A comparison of models to predict annoyance to noise from mixed sources[END_REF]). The presence of the paradox effect in Exp. B may be explained by the presence of natural noises which could have propagated through the partially open window of the experimental cottage. The experiment was carried out during spring and it is likely that birdsong or rustling leafs had a positive effect on total annoyance responses. It has been demonstrated in different studies that natural noises may reduce annoyance. Studying combined industrial and natural noises, Alayrac et al. [START_REF] Alayrac | Total annoyance from an industrial noise source with a main spectral component combined with a background noise[END_REF] identified the paradox effect and explained it by cognitive effects due to the presence of natural noise. Guastavino et al. [START_REF] Guastavino | The ideal urban soundscape: Investigating the sound quality of French cities[END_REF] proved that natural noises may enhance the pleasantness of the sound environment.

Road traffic noise was found to cause more extensive annoyance than tramway noise at the same SPL. Hence, to reach equal annoyance the tramway traffic noises must be louder. This asymmetry occurred in Exp. A, B and C. In a primary study on annoyance due to tramway noises relative to buses and trucks in single noise exposure situations, Sandrock et al. [START_REF] Sandrock | Experimental studies on annoyance caused by noises from trams and buses[END_REF] found that tramway pass-by noise was judged equally annoying as a bus pass-by noise with a 3 dB lower level. In a second study, investigating annoyance due to tramways in combined noise exposure situations, they also found evidence for a tramway bonus. This is corroborated in this work. Annoyance differences observed for combinations of railway and road traffic noise, have led to the so called "railway bonus" which was set to 5 dB(A) referring to the German, French or Austrian legislation for example. However, recent studies did not find evidence for such a bonus (e.g. [START_REF] Coensel | Experimental investigation of noise annoyance caused by highspeed trains[END_REF]). It has been suggested that the railway bonus may be related to the distance between the source and the receiver and the railway bonus may only hold true for large distances from railway tracks [START_REF] Coensel | Experimental investigation of noise annoyance caused by highspeed trains[END_REF]. Regarding both Exp. A and B, the urban road vehicle pass-by noises used for the construction of the traffic sequences were recorded at a shorter distance and a tramway bonus of approximately 6 dB(A) was observed. In Exp. C, a tramway bonus of approximately 10 dB(A) was estimated and the tramway noises were recorded at a shorter distance. Based on these three experiments, the influence of the distance could not be confirmed. In fact, the asymmetry between road traffic noise and tramway traffic noise is assumed to result from the differences in the number of events and also the acoustical characteristics between the two studied noises. Indeed, the temporal and spectral structure of both noises is different. The verbalization task supports the assumption that the number of events may have influenced annoyance responses. In all the studied combinations the number of road vehicle pass-by noises was greater than tramway traffic noises.

Conclusion

The main goal of this work was to highlight and gain understanding of the perceptual phenomena involved in annoyance due to urban road traffic noise combined with tramway noise. For this purpose, three laboratory experiments were conducted. The first experiment, Exp. A, was carried in imaginary conditions. Experiments B and C are carried out in simulated environment employing different procedures, apparatus and stimuli.

In Exp. A, B and C the participants performed a reading activity either in an imaginary situation or a "home-like" simulated environment. The duration of the combined noise source sequences increased steadily from Exp. A (3 min.) to Exp. C (16 min.) approaching in situ contexts. Between the 3 experiments, differences in experimental methodologies existed regarding, for instance, the different sound reproduction systems, the contexts (imaginary versus simulated) between Exp. A and B, or different road traffic noises and tramway traffic noises between Exp. B and C. This current work allowed to study the different perceptual phenomena involved in total annoyance due to combined road traffic and tramway noises using different experimental methodologies. The results show that similar tendencies could be observed throughout the experiments. For instance, for all the experiments it was shown that the road traffic and tramway traffic partial annoyance responses were influenced by each other. Furthermore, the strongest component effect prevailed across the 3 experiments. However, across the experiments different secondary perceptual phenomena were also observed. In Exp. B the paradox effect could be identified in certain combinations and in Exp. A and C the synergistic effect was found. These differences were attributed to the background noise employed in each experiment. In Exp. A and C recorded urban background noise was added to the sequences whereas during Exp. B natural urban background noise was present and birdsong may have reduced total annoyance ratings due to the combination of the noise sources. This phenomenon has been previously observed in studies when community noises were heard in presence of natural noises. To study the perceptual phenomena prevailing in situ, it would be necessary to conduct a field study on annoyance due to urban road traffic and tramway traffic.

In the next chapter, the indicator proposed in Chapter 4.4.3 for the assessment of annoyance due to urban road single-vehicle pass-by noises will be tested for the characterization of annoyance due to urban road traffic in presence of another urban traffic noise source, the tramway traffic.

Chapter 6

Indicators for the prediction of total annoyance due to combined noise exposure

This chapter outlines the construction and application of perceptual and psychophysical total annoyance models in order to predict total annoyance due to the combination of urban road traffic and tramway traffic noises. The total annoyance models will be tested based on the analysis carried out in Chapter 5. Furthermore, the indicator proposed in Chapter 4 for the characterization of urban road single-vehicle pass-by noises will be tested for the characterization of annoyance due to road traffic noise in presence of tramway traffic noise.

Background

The objective of this chapter is to predict total annoyance due to combined road traffic and tramway traffic noises using different relevant total annoyance models established in literature. The models were selected based on the knowledge gained in the previous chapter regarding the observed perceptual phenomena involved in annoyance due to road traffic and tramway traffic noises. Depending on whether total annoyance is associated with perceptual variables or is expressed as a function of acoustic variables, total annoyance models are either perceptual or psychophysical [START_REF] Berglund | Empirical issues concerning annoyance models for combined community noises[END_REF]. Perceptual total annoyance models may underlie measured variables and calculated variables. The total annoyance models will be constructed based on the combined noise exposure Exp. A (see Chapter 5) carried out in an imaginary context. The constructed total annoyance models will be tested in terms of their predictive power using annoyance responses obtained in Exp. B, carried out in a simulated environment. It must be noted that the predictive quality of the proposed models will be limited due to the presence of different phenomena in the two experiments. In addition to the strongest component effect prevailing in both experiments, secondary phenomena could be observed. In some noise combinations of Exp. A the synergistic effect was present and the combined noise sources paradox was found in some noise combinations of Exp. B. The annoyance responses obtained in Exp. C could have provided additional data to assess the predictive quality of the models due to the presence of synergistic effects, however, the small number of stimuli did not allow the testing of these models in accordance with literature.

Selected total annoyance models

Several total annoyance models are proposed in literature studying combined noises (cf. review Marquis-Favre et al. [START_REF] Marquis-Favre | Noise and its effects -a review on qualitative aspects of sound. Part II: Noise and annoyance[END_REF]). Many studies have been performed testing these models with different combination of noises (e.g. [START_REF] Ronnenbaum | Synergetic effects of noise from different sources: A literature study[END_REF][START_REF] Berglund | Loudness (or annoyance) summation of combined community noises[END_REF]). Five different models highlighted as relevant in literature were selected in this study either solely available in a psychophysical form or being also applicable in a perceptual form. The analytical expressions of certain models seem particularly relevant regarding the phenomena observed in Chapter 5. First, the models are briefly described below.

A more detailed review of the models can be found in Chapter 1.

The weighted summation model [START_REF] Vos | Annoyance caused by simultaneous impulse, road-traffic, and aircraft sounds: a quantitative model[END_REF] is only applicable in its psychophysical form. This model translates the noise from the individual sources into the equally annoying sound energy levels of a reference source and then sums these levels [START_REF] Miedema | Relationship between exposure to multiple noise sources and noise annoyance[END_REF].

A T = a + b • L t (6.1)
where L t denotes the "overall or total sound rating" [START_REF] Vos | Annoyance caused by simultaneous impulse, road-traffic, and aircraft sounds: a quantitative model[END_REF] which involves the calculation of level-dependent penalties of the combined noises with respect to a reference noise. This model was tested as it the basis for the annoyance equivalents model (cf. [START_REF] Miedema | Relationship between exposure to multiple noise sources and noise annoyance[END_REF]) recommended for the assessment of annoyance due to different noise sources in the EU [START_REF] Vos | Noise maps in the european union: an overview[END_REF].

The following total annoyance models are perceptual models which can be applied in two versions. They will be tested in a measured perceptual form by using mean measured partial annoyance, and in a calculated perceptual form using calculated partial annoyance. Thus, the variables A 1 and A 2 may express measured partial annoyances or calculated partial annoyances.

The strongest component model states that total annoyance is equal to the maximal partial annoyance of the noise combination.

A T = max(A 1 , A 2 ) (6.2)
In this study, this model is expected to yield good prediction of total annoyance responses as total annoyance was found to correspond to the strongest component for most of combinations of road traffic and tramway traffic noises. Due to its analytical expression it can only take into account the strongest component effect and cannot anticipate other effects.

The vector summation model states that total annoyance is derived from a vector summation of the specific or partial annoyances of the combined noises.

A T = a • A 1 2 + A 2 2 + 2 • A 1 A 2 cos α 1,2 0.5 + b (6.3)
where alpha depends on the noise sources in combination. Depending on the combination of noises, different values for alpha have been found to result in strong model fits. Morel [START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF], for instance, obtained good results with α 1,2 equal to 101°studying urban road traffic noise combined with industrial noise. Some studies, however, (e.g. [START_REF] Ronnenbaum | Synergetic effects of noise from different sources: A literature study[END_REF]) found no support of this model which may be due to the fact that it cannot account for different contributions of specific or partial annoyances.

The linear regression model states that total annoyance corresponds to a weighted sum of specific annoyances [START_REF] Berglund | Total annoyance and perceptually discernible noise sources[END_REF].

A T = a 1 A 1 + a 2 A 2 + b (6.4)
The perceptual version of the mixed model proposed by Morel et al. [START_REF] Morel | A laboratory study on total noise annoyance due to combined industrial noises[END_REF] takes the following form (cf. [START_REF] Pierrette | Noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF][START_REF] Pierrette | Corrigendum to noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF]):

A T = aA 1 + bA 2 + c|A 1 -A 2 | + d (6.5)
This model includes an interaction term which is the absolute difference between either the measured partial annoyances or the difference between calculated partial annoyances. Pierrette et al. [START_REF] Pierrette | Noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF][START_REF] Pierrette | Corrigendum to noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF] showed that this model is an improvement compared to the linear regression model when effects different than the strongest component effect are observed.

Proposition of total annoyance models for combined urban road and tramway traffic noises

The independent variables of the measured perceptual models are the measured partial annoyances for urban road traffic and for tramway traffic. As stated above, the calculated form of the total annoyance models, makes use of indices to calculate partial annoyances due to road traffic or tramway. In the following, the calculation of partial annoyances will be detailed. Then, the construction of the total annoyance models will be elaborated.

Calculation of partial annoyance responses

Prior to the prediction of total annoyance due to a combination of noises using perceptual models, partial annoyance due to each noise must be calculated. The importance of characterizing different types of vehicles for calculating annoyance due to urban road traffic noise has been shown previously 1 (see Chapter 5). In order to take into account the different influential auditory attributes of road traffic noises and of tramway traffic noises, partial annoyances will be calculated based on the indicators established in single noise exposure experiments. This approach has one main underlying assumption: partial annoyance is approximated by specific annoyance as it is implicitly considered in different studies dealing with combined noise exposure (cf. [START_REF] Miedema | Relationship between exposure to multiple noise sources and noise annoyance[END_REF][START_REF] Vos | Annoyance caused by simultaneous impulse, road-traffic, and aircraft sounds: a quantitative model[END_REF]). Nevertheless, results from Section 5.2.2.1 and 5.3.2.1 show that partial annoyance maybe be slightly affected by the noise in the combination. Such an assumption may thus limit the predictive power of the total annoyance models using the calculated partial annoyances.

Urban road traffic partial annoyance

For the calculation of partial noise annoyance due to road traffic noises the indicator proposed in Chapter 4 for the assessment of annoyance due to urban road vehicle pass-by noises will be used on the basis of the results of Gille and Marquis-Favre [START_REF] Gille | Étude de l'influence de l'ordre de passage successif des véhicules routiers urbains sur la gêne sonore[END_REF]: Road traffic noise annoyance is conditioned by the "worst" event within the noise sequence. As shown in Chapter 4, the combination of indices accounts for different annoyance-relevant acoustical/psychoacoustical features: mean loudness (N mean ), the Total Energy of Tonal Components within critical bands from 16 to 24 Barks (here denoted T ET C 16-24 ), the "sputtering" index (m sputt,10 ) and the "nasal" index (m nas,10 ) (cf. Model 1, Table 4.5). The indicator used for the characterization of urban road traffic partial annoyance is of the following form:

A RT = 0.50 the newly proposed indices and the constructed indicator for annoyance due to urban road traffic noise.

Tramway traffic partial annoyance

For the calculation of partial annoyance due to the tramway traffic noises used in this study, the annoyance indicator proposed by Trollé et al. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF][START_REF] Trollé | Corrigendum to short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF] for pass-by noises of tramways in different operating configurations will be used with its grand-mean centered values (see [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF][START_REF] Trollé | Corrigendum to short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF] To assess the accuracy of this indicator to characterize the tramway traffic partial annoyance of Experiment A, a comparison between calculated and measured partial tramway traffic annoyance is shown in Figure 6.2. It can be seen that a slight underestimation occurs for the tramway traffic noises T1(46), T2(48) and 2xT1 [START_REF] Izumi | Annoyance due to mixed source noises -a laboratory study and field survey on the annoyance of road traffic and railway noise[END_REF]. The calculated partial annoyance appears to be in good agreement with the measured partial annoyance. The correlation coefficient determined between the calculated and the measured partial annoyance responses is, nevertheless, non-significant For both L Aeq (a) and T ET C 12-24 (b) data points are clustered closely to the regression line. The L Aeq accounts for the intensity sensation which increases with an increasing SPL. The T ET C 12-24 index characterizes the annoyance-relevant highfrequency content due to squeal noise which is maximal for T3(50) and 2xT3 [START_REF] Kaku | Predicting overall reaction to multiple noise sources[END_REF]. Hence, these indices are shown to be relevant based on the small amount of data points (only five points). The L Aeq correlates significantly with partial annoyance (r = 0.93; p < 0.001) whereas the T ET C 12-24 index is not significantly correlated (r = 0.78; p = 0.13). The non-significant correlation obtained using the current set of five tramway traffic noises appears to be related to the small amount of data points. With a greater corpus of stimuli, the combination of these indices is relevant and expected to characterize annoyance adequately (cf. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF][START_REF] Trollé | Corrigendum to short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF]).

T1(46) T2 (
In the following, these annoyance indicators will be used as independent variables to construct the total annoyance models. 3 An indicator comprising N mean , V AP norm and T ET C 12-24 has been proposed to characterize annoyance due to tramways in in-curve operating configurations (cf. [START_REF] Trollé | Indicateurs acoustiques de la gêne en situation de mono-exposition au bruit de tramway[END_REF]). This indicator was also tested based on the measured partial annoyance responses and proved not to be significantly correlated either (r = 0.81; p = 0.1) as the indicator tested here above. This seems to be due to the small amount of data points used to test this indicator. As the indicator presented in Eq. 6.7 was developed for tramways in different operating configurations rather than just one operating configuration, it was selected for the calculation of tramway traffic partial annoyance responses and the construction of the total annoyance models.

Construction of total annoyance models

For the construction of the perceptual and psychophysical total annoyance models, the independent variables are inserted into the regression models. For the measured version of the perceptual models, the independent variables represent measured partial annoyances. For the calculated version of the perceptual models, the independent variables are the calculated road traffic and tramway traffic partial annoyance detailed in Eqs. 6.6 and 6.7, respectively. The total annoyance models are assessed based on the determination coefficient, the adjusted determination coefficient (R 2 adj ) and the standard error of the estimate (SE). These parameters result from the regression analysis carried out between total annoyance responses collected during Experiment A, and the independent variables. The higher the R 2 and the R 2 adj and the lower the SE, the better is the goodness-of-fit of the total annoyance model. The corresponding correlation coefficient, the intercept and the slope determined between the measured and the calculated annoyance provide relative measures for the potential of each model to predict annoyance. A good prediction potential would be obtained with an intercept close to 0 and a slope close to 1. Specific model-related parameters are required for two of the tested models. For the weighted summation model the k factor must be determined. In recent literature k = 7 [START_REF] Morel | Annoyance due to combined road traffic and industrial noises: a simulated environment experiment[END_REF], k = 10 [5, 79] and k = 15 [START_REF] Vos | Annoyance caused by simultaneous impulse, road-traffic, and aircraft sounds: a quantitative model[END_REF] were found to yield good results. In this study the best goodness-of-fit with the data of Exp. B could be obtained with k = 15. The linear regression coefficients of the weighted summation model (cf. Section 1.4.3.1) were classically obtained from the linear relationship between the SPL of the reference noise (road traffic noise) and the annoyance ratings collected during the single noise exposure experiment (for the reference noise: Chapter 4).

The vector summation model in its measured perceptual form could be opti-partial annoyance A TT . Hence, this model will be excluded from further analysis.

Testing of total annoyance models

The total annoyance models previously constructed are applied to annoyance data obtained in the simulated environment experiment (Exp. B). The prediction quality of the total annoyance models will be assessed. The models are compared based on the Bravais-Pearson correlation coefficient (r ), the slope and the intercept resulting from the correlation and regression analysis between measured total annoyance responses from Exp. B and the predicted total annoyance responses obtained by applying the models constructed in the previous section (cf. Section 6.3.2). Table 6.2 shows the results of the model comparison in terms of prediction quality. Table 6.2: Assessment of the prediction quality of the measured perceptual (perc. meas.), the calculated perceptual (perc. calc.) and the psychophysical total annoyance models. The correlation coefficient r and the regression line determined between the measured total annoyance from Experiment B and the predicted annoyance are provided. a p < 0.001, b p < 0.01, c p < 0.05. The total annoyance models in both measured and calculated versions strongly correlate with the measured total annoyance except the weighted summation model. Among the measured perceptual models, the best prediction quality can be obtained with the strongest component model. The strongest component model also yields the best results among the calculated perceptual models tested. As can be seen, the data points are closely clustered to the regression line but a very small overestimation of total annoyance can be observed. Both models overestimate total annoyance, however, the overestimation is moderate with the strongest component model.

Discussion and conclusion

Regarding measured perceptual total annoyance models, this study demonstrated that the strongest component model explains annoyance responses very well. Such a result was expected as the strongest component effect is the main perceptual phenomenon observed for the different combined noise source situations studied. When one noise source is much more annoying than the other, it was found that the strongest component model predicts total annoyance well both in laboratory experiments and field studies [START_REF] Berglund | Loudness (or annoyance) summation of combined community noises[END_REF][START_REF] Lan | Social survey on community response to aircraft noise in Ho Chi Minh City[END_REF][START_REF] Botteldooren | Fuzzy models for accumulation of reported community noise annoyance from combined sources[END_REF]. It must be noted that, besides the strongest component effect, the two experiments used for the construction and application of the total annoyance models exhibit different secondary perceptual phenomena. The mixed model allows for the anticipation of synergistic effects which is represented by a strong model fit regarding Exp. A (see Table 6.1). On the contrary, it cannot accommodate for the paradox phenomenon observed in Exp. B as this effect was not present in the annoyance data of Exp. A. This explains the slight overestimation of all the models.

For the calculated versions of the perceptual models, the road traffic and tramway traffic partial annoyance responses were predicted using combinations of indices established in single noise exposure experiments (cf. Chapter 4 and [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF][START_REF] Trollé | Corrigendum to short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF]). It was shown that the calculated road traffic partial annoyance was significantly correlated with the measured road traffic partial annoyance. The calculated tramway partial annoyance, however, was not significantly correlated with measured tramway partial annoyance. Through a point-by-point comparison with the measured tramway partial annoyance responses, it could be shown that the calculated tramway partial annoyance responses yielded meaningful results. Furthermore, the indices characterizing the annoyance-relevant sensations evoked by tramway pass-by noises proved to be relevant when their variations were compared with the variations of measured tramway partial annoyance. The lack of significant correlation coefficients was due to small number of data points. This approach makes sense from a practical point of view. The issue present in in situ measurement campaigns for combined noises is that combined noises mostly occur simultaneously, thus making it difficult to measure single noises in isolation. In such a case, it is necessary to characterize these noise in single noise exposure situations and apply this characterization to combined noises (cf. [START_REF] Vos | Annoyance caused by simultaneous impulse, road-traffic, and aircraft sounds: a quantitative model[END_REF][START_REF] Miedema | Relationship between exposure to multiple noise sources and noise annoyance[END_REF]).

The weighted summation model by Vos [START_REF] Vos | Annoyance caused by simultaneous impulse, road-traffic, and aircraft sounds: a quantitative model[END_REF] and the annoyance equivalents model [START_REF] Miedema | Relationship between exposure to multiple noise sources and noise annoyance[END_REF] have been widely accepted as the representative total annoyance model for different noise sources in the EU [START_REF] Vos | Noise maps in the european union: an overview[END_REF]. This study indicated that total annoyance prediction may be more accurate based on perceptual total annoyance models which involve the characterization of each noise source independently using indicators created in single noise exposure experiments. This is congruent with other studies which showed that perceptual models predict total annoyance more adequately than psychophysical models ( [START_REF] Pierrette | Noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF][START_REF] Pierrette | Corrigendum to noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF] in situ and [START_REF] Morel | Annoyance due to combined road traffic and industrial noises: a simulated environment experiment[END_REF] in laboratory conditions). In the current study, it could be observed, however, that the perceptual models overestimated total annoyance responses. Among these models the strongest component model yielded the best results. This small overestimation regarding the calculated perceptual total annoyance models may be explained by the principal assumption underlying the approach of characterizing partial annoyances based on combination of indices created in single noise exposure experiments. It was assumed that partial annoyance can be approximated by specific annoyance (as found in other studies) but this did not always hold true. Even though this approach allows for the characterization of important annoyance-related auditory attributes (cf. [START_REF] Gille | Étude de l'influence de l'ordre de passage successif des véhicules routiers urbains sur la gêne sonore[END_REF]) it may not always be adequate to apply it as it neglects potential interaction effects between the noises in the combination (i.e. road traffic noise influences tramway partial annoyance and the reverse could also be observed). As shown in Chapter 5, such effects between partial annoyances could be observed for both Exp. A and B.

In the current study, the strongest component model provided a good prediction of total annoyance although it does not accommodate for perceptual phenomena such as synergistic and paradox effects. As indicated before, one of the effects was observed in the data set used for the construction, and the other one in the data set used for the annoyance prediction. Thus, the different models generally capable to account for such perceptual effects were not tested under optimal conditions. Several field studies also reported that the strongest component model yielded good prediction of total annoyance compared to other models (e.g. [START_REF] Nguyen | Community response to aircraft and combined noises in Hanoi[END_REF][START_REF] Nguyen | Comparison of models to predict annoyance from combined noise in Ho Chi Minh City and Hanoi[END_REF]). It must be noted that the strongest component model takes into account the most annoying noise source within a combination, but it is neglecting the contribution of the other noise source of the combination (cf. [START_REF] Pierrette | Noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF][START_REF] Pierrette | Corrigendum to noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF]). In the field study conducted by Pierrette et al. [START_REF] Pierrette | Noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF][START_REF] Pierrette | Corrigendum to noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF], the strongest component effect was predominant. Consequently, the strongest component model was found to predict total annoyance most adequately, however, it did not take into account 28 % of the respondents who considered the other noise source of the combination equally annoying as the strongest component. In this study, the strongest component model would neglect those participants who considered the tramway noise as annoying as the road traffic noise (e.g. "the road traffic noises could be as annoying as the tramway noises" pointed out in the verbalization task of Exp. A, cf. Section 5.2.2.3). scale pilot experiment, the performance of the two different roughness models in terms of roughness characterization was tested. The results obtained confirmed the observation that both roughness and fluctuation strength are generally not adapted for the characterization of roughness evoked by urban road vehicle pass-by noises.

Chapter 3 further investigated the various noise characteristics influencing annoyance due to urban road vehicle pass-by noises. An experimental methodology was adopted combining a semantic differential test with a verbalization task. Based on the different item scale ratings of the semantic differential test, the dimensions underlying the perceptual space of the tested urban road single-vehicle pass-by noises could be identified. Besides hedonic judgments, the spectral and temporal characteristics as well as the movement of the source (related to the driving behaviour) proved to play an important role in formulating noise annoyance judgements. Through the verbalization task, associated with the item scale of annoyance, additional descriptions of potential factors influencing noise annoyance could be gathered; the spectral sensation "dull/shrill" as well as the two different modulation-related sensations "sputtering" and "nasal" emerged influencing noise annoyance judgments. These were especially noticed for powered-two-wheelers in acceleration. The sensation "dull/shrill" related to high frequency content, could be characterized using the index TETC within critical bands from 16 to 24 Barks. It could be shown that both sensations "sputtering" and "nasal" were due to amplitude modulations. The correlation of "sputtering" and "nasal" sensations with psychoacoustic indices was unsatisfactory, supporting the findings of the previous Chapter. To adequately characterize these sensations, suitable "sputtering" and "nasal" indices (m sputt,10 and m nas,10 , respectively) were proposed. The indices were not only found to highly correlate with these annoyance-relevant sensations, their characterization was also more meaningful compared to psychoacoustic models of fluctuation strength and roughness. The following chapter tested the relevance of these indices in combination with other suitable indices.

In Chapter 4 the proposed indices were validated based on two experiments employing a broad range of urban road single-vehicle pass-by noises. First, to point out annoyance-relevant noise characteristics, other than loudness, the indices were tested using data obtained in a laboratory experiment in which urban road vehicle pass-by noises were reproduced without level differences between vehicles. By testing all the indices considered in the previous chapter, it could be shown that both "sputtering" and "nasal" indices as well as the TETC index correlated significantly with mean annoyance responses. An indicator comprising m sputt,10 , m nas,10 and TETC proved to calculate mean annoyance responses better than an indicator including F mean , R max and TETC. In a second step, the validity of the indices was assessed using pass-by noises with level differences corresponding to sound pressure levels measured in situ. It could be shown that intensity was accurately characterized using mean loudness. Based on partial correlations controlling for mean loudness, the indices previously considered were tested. The importance of the indices m sputt,10 and m nas,10 and TETC was demonstrated. An indicator comprising N mean and the proposed indices m sputt,10 , m nas,10 and TETC was shown to yield a better goodness-of-fit than an indicator comprising N mean , F 10 and TETC. This was attributed to the independence of the indices m sputt,10 , m nas,10 from loudness. The results of the second experiment also showed the benefit of using loudness and the proposed indices as opposed to L Aeq to better characterize noise annoyance due to urban road vehicle pass-by noises.

The second part of this thesis focused on annoyance due to urban road traffic noise heard in presence of tramway noise. The aim was to test the proposed indicator for the characterization of road traffic noise heard in presence of tramway noise. For the characterization of tramway noise, this work could benefit from an annoyance characterization of tramway pass-by noises carried out by Trollé et al. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF][START_REF] Trollé | Corrigendum to short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF]. The analysis concentrated first on the study of the perceptual phenomena involved in total annoyance due to urban road traffic noise combined with tramway noise (Chapter 5). The final step consisted in proposing appropriate models for the prediction of total annoyance due to these noise sources (Chapter 6) based on the findings of the previous chapters regarding the characterization of annoyance due to road traffic noise and based on the findings from literature [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF][START_REF] Trollé | Corrigendum to short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF] regarding the characterization of annoyance due to tramway noise. Prior to analyzing the perceptual phenomena, it is important to highlight the main differences between the experimental methodologies employed in the three experiments used to study the phenomena. Experiment A was conducted in an imaginary context with short stimuli durations (3 min.); Experiments B and C were conducted in simulated, more realistic contexts with longer durations of stimuli (6 min. and 16 min., respectively). In Experiment B the stimuli were reproduced through loudspeakers and a subwoofer placed outside an experimental cottage. The window facing the loudspeakers was left partially open simulating the worst noise exposure situation at home [START_REF] Alayrac | Annoyance from industrial noise: indicators for a wide range of industrial sources[END_REF]. This allowed the reproduced noise sequences and urban background noise to propagate into the simulated living room. In Experiment C, the sequences were played back through a wavefield synthesis system with urban background noise added to the sequences. This experiment was also carried out in an experimental living room (LSEE, IFSTTAR Bron). In each experiment, the road and tramway traffic sequences were constructed based on traffic densities representative of peak and off-peak hours in a French city.

The different experiments led to similar tendencies regarding partial and total annoyance responses. It could be observed that the partial annoyances were influenced by each other. Generally, at high road traffic densities (corresponding to high road traffic levels for Experiment A and B), partial annoyance due to tramway noise tended to increase. Road traffic noise was generally found to cause more extensive annoyance than tramway noise at the same SPL. Hence, to obtain equal annoyance the tramway traffic noises must be louder. Furthermore, the importance of accounting for different types of urban road vehicles in noise annoyance assessment was shown. Regarding the partial annoyance responses in relation to total annoyance, the strongest component effect prevailed throughout the three experiments, i.e. total annoyance was mostly approximated by the strongest component within the combination. However, in some combinations of Experiment A and C synergistic effects could be observed and total annoyance was found to be greater than the strongest component. In Experiment B the combined noise source paradox was observed, i.e. in some combinations total annoyance was found to be smaller than the strongest component. This was attributed to the fact that natural noises, such as birdsong or rustling leaves, could have been present which influenced the annoyance judgments. In fact, natural sounds are known to increase the pleasantness of sounds (cf. [START_REF] Guastavino | The ideal urban soundscape: Investigating the sound quality of French cities[END_REF]) and to reduce total annoyance ratings (cf. [START_REF] Alayrac | Total annoyance from an industrial noise source with a main spectral component combined with a background noise[END_REF]).

Chapter 6 dealt with the prediction of total annoyance using perceptual and psychophysical models. Generally, psychophysical models are found not to successfully predict total annoyance, especially in situ (e.g. [START_REF] Pierrette | Noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF][START_REF] Pierrette | Corrigendum to noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF]. However, the weighted summation model was tested as it is the basis for the total annoyance model recommended by the EU [START_REF] Vos | Noise maps in the european union: an overview[END_REF]. The results obtained from these laboratory experiments show that the prediction quality of the perceptual models was higher than the weighted summation model. The perceptual models were tested in two forms: partial annoyance responses used for the construction of the total annoyance models were either measured (measured perceptual form) or calculated (calculated perceptual form). The total annoyance models in both versions were constructed based on the annoyance responses from Experiment A and their prediction quality was assessed based on the responses from Experiment B. In the measured perceptual form, both the strongest component model and the mixed model predicted annoyance responses very accurately. To predict total annoyance in field conditions, the measured perceptual total annoyance models would require the collection of partial annoyance responses from residents. From a practical perspective it would be highly advantageous (less time consuming, cost effective) to predict total annoyance based on calculated partial annoyance responses. These could be calculated from recordings of road traffic or tramway traffic noise. The total annoyance models in their calculated perceptual form are based on calculated partial annoyance responses. It was shown that partial annoyance due to urban road traffic noise could be accurately characterized using the noise annoyance indicator proposed in Chapter 4 comprising N mean , m sputt,10 , m nas,10 and TETC. Partial annoyance due to tramway traffic noise could be sufficiently characterized using a noise annoyance indicator proposed by Trollé [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF] comprising L Aeq and the T ET C index within critical bands from 12 to 24 Barks. Among the calculated perceptual total annoyance models tested, the strongest component model predicted total annoyance responses most accurately, however, resulting in a slight overestimation and not always in agreement with participants' verbalizations. One limitation of the strongest component model is that it cannot take into account synergistic or paradox effects. Furthermore, this model was found not to be in agreement with one third of the residents' judgments gathered in a survey [START_REF] Pierrette | Noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF][START_REF] Pierrette | Corrigendum to noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF]. Thus, further research is needed in order to overcome these limitations and to propose total annoyance models that adequately characterize perceived annoyance due to combinations of different noises.

Perspectives

This thesis contributed to the improvement of the physical and perceptual characterization of annoyance due to urban road traffic noise by accounting for annoyancerelevant sensations. It could be shown that the assessment of noise annoyance due to urban road traffic noise would benefit from considering the proposed indicator by taking into account different acoustical factors. Furthermore, the models for the prediction of total annoyance due to urban road traffic and tramway traffic noises provided accurate prediction of total annoyance. However, it could be shown that further research is needed before the final objective, the production of noise annoyance maps, is achieved.

Creating maps reflecting the annoyance potential of residents in certain areas would transform a rather abstract mathematical concept like the L den index, currently implemented in strategic noise maps, into a concept people can actually grasp. The proposed indicator tested in laboratory conditions proved to enhance the assessment of annoyance due to single road vehicle pass-by noises and also annoyance due to constructed road traffic noise. Before applying the indicator on in situ data, further testing in controlled conditions is required using various traffic noise scenarios.

Then, in a second instance, non-acoustical factors contributing to perceived annoyance, such as noise sensitivity, should be considered. The WHO [124] states that considering such factors may lead to improved dose-response relationships. Noise sensitivity in the context of noise annoyance has already been considered in previous studies conducted in laboratory conditions (e.g. [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF][START_REF] Trollé | Corrigendum to short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF][START_REF] Sandrock | Experimental studies on annoyance caused by noises from trams and buses[END_REF]). The relevance of taking this factor into account in a model to predict noise annoyance has to yet be tested using in situ data. Noise sensitivity ratings could be collected by using continuous scales ranging from "0" (not at all sensitive) to "10" (extremely sensitive) during a survey as done by Ecotière et al. [START_REF] Ecotière | Multi-exposition au bruit des transports -application et évaluation d'une méthode opérationelle d'intification de la gêne en situation de multi-exposition[END_REF]. The ratings could be incorporated in a noise annoyance indicator through multilevel regression analysis. To predict noise annoyance by taking into account different acoustical factors, such an indicator would require noise recordings in order to determine the values of the relevant indices. Short durations (between 10 to 20 minutes) may be sufficient to characterize urban acoustic environments [START_REF] Brocolini | Measurements of acoustic environments for urban soundscapes: Choice of homogeneous periods, optimization of durations, and selection of indicators[END_REF] at different locations.

To complete the adequate annoyance assessment, it is necessary to expand the research to different transportation noises in order to identify their acoustical features influencing noise annoyance. The approach presented in this thesis is suitable to identify these annoyance-relevant acoustical features. This is a perspective to improve current energy-related indices which are insufficient to successfully characterize annoyance.

Concerning total annoyance models, generally, it could be shown that perceptual total annoyance models predict total annoyance due to the combined noises better than psychophysical models. This emphasizes the need to improve the characterization of noise annoyance due to noise sources in single noise exposure by taking into account various annoyance-relevant acoustical factors and relevant non-acoustical factors through multilevel regression analysis. This work showed that different perceptual phenomena are involved in total annoyance judgments due to road traffic and tramway traffic noises which may affect the prediction quality of the total annoyance models. In order to gain more knowledge about the perceptual phenomena prevailing in field conditions, it is necessary to further investigate the perceptual features involved in total annoyance due to road and tramway traffic noise. Based on this knowledge, the proposed perceptual models tested in this work could be refined in order to take into account these effects.

A.1 Auditory spectrograms of the most representative pass-by noises [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF] The following Auditory spectrograms show the frequency distribution of the most representative urban road single-vehicle pass-by noises per perceptual category as determined by Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF]. All these pass-by noises were employed both in the semantic differential test (cf. Chapter 3) and in Experiment 1 (cf. Chapter 4). The road traffic noise sequences constructed for Experiment A (cf. Chapter 5) were also composed of these pass-by noises, except dfo_4, pao_1 and vau_5.

A.3 Auditory spectrograms of complementary passby noises used in Experiment 1

The following figures show the ASPs of two urban road single-vehicle pass-by noises used in Experiment 1 (in addition to the ones presented in Appendix A.1 and A.2; cf. Chapter 4). These pass-by noises were also employed in Experiment A (cf. Chapter 5), except ddu_2.

Personal references

Articles in international peer-reviewed journals Articles in preparation for publication in international peer-reviewed journals

• Achim Klein, Catherine Marquis-Favre, Patricia Champelovier. Assessment of annoyance due to urban road traffic noise combined with tramway noise.

• Laure-Anne Gille, Catherine Marquis-Favre, Achim Klein. Noise annoyance due to urban road traffic with powered-two-wheelers: quiet periods, order and number of vehicles.

Articles in conference proceedings Résumé : La pollution sonore est un problème majeur pour les résidents des zones urbaines. La directive Européenne 2002/49/CE impose aux états membres l'établissement de cartes de bruit. Ces dernières sont construites sur la base de l'indice énergétique L den , également utilisé dans les relations dose-effet établies pour prédire dire la gêne. Toutefois, pour l'évaluation de la gêne due au bruit dans les zones urbaines, la pertinence de cet indice est souvent remise en question. En effet, de nombreuses études ont montré que les caractéristiques temporelles et spectrales des bruits environnementaux influencent aussi les réponses de gêne et ne sont pas prises en compte dans cet indice. Cette thèse vise à contribuer à l'amélioration de la caractérisation de la gêne due au bruit des véhicules routiers en ville. Elle est basée sur des expériences réalisées en laboratoire. Elle comprend deux parties principales. La première partie a comme objectif de progresser sur la caractérisation physique et perceptive du bruit des passages de divers véhicules routiers en milieu urbain tels que les bus, les deux-roues, les poids lourds et les véhicules légers. Une attention particulière a été portée sur la caractérisation de la gêne due aux deux-roues qui sont parmi les véhicules routiers les plus gênants et sont peu étudiés dans la littérature. Dans cette perspective, un indicateur acoustique caractéristique de la gêne due au bruit routier urbain a été déterminé : il rend compte de différents attributs auditifs gênants en associant la sonie, un indice spectral et deux indices de modulation proposés dans le cadre de ces travaux. Dans les zones urbaines, les riverains sont souvent exposés à la circulation routière en présence d'autres sources de bruit de l'environnement. L'objectif principal de la deuxième partie est la prédiction de la gêne totale due au bruit du trafic routier urbain combiné avec le bruit de tramway. Dans le but de caractériser la gêne totale, les phénomènes perceptifs liés à la combinaison de ces bruits sont tout d'abord étudiés. Ensuite, l'indicateur proposé précédemment pour caractériser la gêne due au bruit des passages de différents véhicules routiers est testé lorsque différents trafics routiers urbains sont considérés. Sur la base de ces résultats, des modèles permettant de caractériser la gêne due au bruit de trafic routier urbain combiné au bruit de tramway ont été proposés. Mots-clés : la gêne de court terme, caractérisation physique et perceptive, bruit de passage de véhicules routiers en milieu urbain, deux-roues motorisés, bruit de trafic routier urbain, indice spectral, indice de modulation, bruit de tramway, mono-exposition, multi-exposition au bruit, gêne totale, expérimentation en laboratoire, environnement simulé Laboratoire de recherche : Laboratoire Génie Civil et Bâtiment (LGCB) de

1. 1

 1 (a) Total annoyance for G presented in combination with T at an L eq of 40 dB(A), as a function of the L eq of G. The linear regression function for G only and the annoyance caused by T only are given as references. (b) Total annoyance for T presented in combination with G at an L eq of 40 dB(A), as a function of the L eq of T. The linear regression function for T only and the annoyance caused by G only are given as references. In both panels (a) and (b), the solid line represents the prediction of the model with k = 16. (Source: Vos [121]) . . . . . . . . . . . . . . . . . . . . . 2.1 Roughness over time of one powered-two-wheeler passing by at constant speed (Category 1). Roughness is computed using an RSL of 200 ms (a) and an RSL of 500 ms (b). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Relationship between measured roughness (median values) and calculated roughness R 90 for 33 pass-by noises of all the seven perceptual categories reproduced at 60 dB(A). Calculated roughness values were obtained using the Oldenburg roughness model. . . . . . . . . . . . . . . . 2.3 Modulation spectra of the pass-by noises dao_2 (a) and dau_5 (b). . . . . 2.4 Relationship between measured roughness (median values) and calculated roughness R mean for 33 pass-by noises of all the seven perceptual categories reproduced at 60 dB(A). Calculated roughness values were obtained using the dBSonic roughness model. . . . . . . . . . . . . . . . . . 3.1 Screenshot of the user interface showing the 25 scales in French language.

3. 5

 5 Modulation spectra determined over the total duration of the single pass-by noises. (a) noise dau_5 and (b) noise vao_3 are characterized as "sputtering"; (c) noise dfu_10 and (d) noise dao_2 are described as "nasal".

4. 10 87 5. 2

 10872 Mean annoyance responses plotted against the values of the indices characterizing modulation sensations for the 33 urban road single-vehicle pass-by noises. (a) m sputt,10 (r = 0.5; p < 0.01), (b) m nas,10 (r = 0.4; p < 0.05). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5.1 Mean measured partial annoyance due to road traffic noise as a function of the road traffic noises (a) and as a function of the tramway traffic noises (b). The standard error is presented along each mean value. . . . Mean measured partial annoyance due to tramway traffic noise as a function of the tramway traffic noises (a) and as a function of the road traffic noises (b). The standard error is presented along each mean value. 5.3 Mean measured total annoyance due to the combination of road traffic and tramway traffic noises (a) as a function of the road traffic noises and (b) as a function of the tramway traffic noises. The standard error is presented along each mean value. . . . . . . . . . . . . . . . . . . . . . . . 5.4 Interaction between the factors RT and TT. . . . . . . . . . . . . . . . . . . 5.5 Vos' representation for total annoyance as a function of the SPL of the tramways for the combinations RT2(46)+T(X) and RT4(52)+T(X) . . . . . 5.6 Experimental set-up of the experimental living room. . . . . . . . . . . . . 5.7 Loudspeaker set-up in front of the façade of the experimental cottage at the campus of the ENTPE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.8 Mean measured partial annoyance due to road traffic noise as a function of the road traffic noises. The standard error is presented along each mean value. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.9 Mean measured partial annoyance due to tramway traffic noise as a function of the tramway traffic noises (a) and as a function of the road traffic noises (b). The standard error is presented along each mean value. 5.10 Mean measured total annoyance due to combined road traffic and tramway traffic noises as a function of the road traffic noises (a) and as a function of the tramway traffic noises (b). The standard error is presented along each mean value. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.11 Vos' representation for total annoyance as a function of the SPL of the tramways for the combinations RT1(48)+T(X) and RT4(58)+T(X) . . . . . 5.12 Vos' representation for total annoyance as a function of the SPL of the tramways for the combinations R-4T and R-9T. . . . . . . . . . . . . . . . 5.13 Vos' representation for total annoyance as a function of the SPL of the tramways for the combinations R+4T and R+9T. . . . . . . . . . . . . . . . 6.1 Comparison between measured road traffic partial annoyances (A RT measured) obtained in Exp. A and calculated partial annoyance due to road traffic noise (A RT calculated). . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Comparison between measured road traffic partial annoyances (A TT measured) obtained in Exp. A and calculated partial annoyance due to road traffic noise (A TT calculated). . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Relationship between measured tramway partial annoyance and the L Aeq (a) and the T ET C 12-24 index (b). . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Total annoyance responses predicted using the measured perceptual form of the strongest component model (r = 0.99; p < 0.001) and the mixed model (r = 0.99; p < 0.001) as a function of the measured total annoyance responses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

84 5. 2 84 5. 3 93 5. 4 102 6. 1 119 B. 1

 84284393410211191 a p < 0.001, b p < 0.01, c p < 0.05. 78 5.1 The road traffic noise sequences and their corresponding SPLs (L Aeq ), traffic densities, number of pass-bys and traffic compositions considering peak and off-peak hours of road traffic in a city; LV: Light vehicles, PTW: powered-two-wheelers, HV: heavy vehicles, B: buses. . . . . . . . . . . . . The tramway noise sequences and their corresponding SPLs (L Aeq ), traffic densities and number of pass-bys considering peak and off-peak hours of tramway traffic in a city. . . . . . . . . . . . . . . . . . . . . . . . . . . . . The road traffic noise sequences and their corresponding SPLs (L Aeq ), traffic densities, number of pass-bys and traffic compositions considering peak and off-peak hours of road traffic in a city; LV: Light vehicles, PTW: powered-two-wheelers, HV: heavy vehicles, B: buses. . . . . . . . . . . . . The tramway noise sequences and their corresponding SPLs (L Aeq ), traffic densities and number of pass-bys considering peak and off-peak hours of tramway traffic in a city. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 5.5 Sequences with combined road and tramway traffic noises and their corresponding SPLs (L Aeq ). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Construction and comparison of the model fit of the selected total annoyance models in their measured perceptual (perc. meas), calculated perceptual (perc. calc.) and psychophysical (psy.) form. The standardized regression coefficients are shown in brackets. The model fit is represented by the determination coefficient R 2 , the adjusted determination coefficient R 2 adj and the standard error SE. r : the Bravais-Pearson correlation coefficient between the measured annoyance ratings and the calculated annoyance ratings. The regression equations shown in brackets are the models with the standardized regression coefficients. a p < 0.001, b p < 0.01, c p < 0.05. . . . . . . . . . . . . . . . . . . . . . . . . 118 6.2 Assessment of the prediction quality of the measured perceptual (perc. meas.), the calculated perceptual (perc. calc.) and the psychophysical total annoyance models. The correlation coefficient r and the regression line determined between the measured total annoyance from Experiment B and the predicted annoyance are provided. a p < 0.001, b p < 0.01, c p < 0.05. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pass-by noises employed for the construction of the urban road traffic noise sequences and their respective occurrences per sequence. LV: Light vehicles, PTW: powered-two-wheelers, HV: heavy vehicles, B: bus. 141

  Figures 1.1

  (a) and (b) illustrate the results of two noises in combination. In Figure 1.1(a) road traffic is fixed at 40 dB(A) and Figure 1.1(b) gunfire noise is fixed at 40 dB(A), their respective specific annoyance measured for this fixed level is shown by a dashed horizontal line. The circles shown in both figures represent the measured total annoyance ratings. The corresponding regression lines are calculated between the SPL and the specific annoyance due to the noise varying in SPL. The solid line represents the predicted total annoyance obtained with the annoyance equivalents model.

Figure 1 . 1 :

 11 Figure 1.1: (a) Total annoyance for G presented in combination with T at an L eq of 40 dB(A), as a function of the L eq of G. The linear regression function for G only and the annoyance caused by T only are given as references. (b) Total annoyance for T presented in combination with G at an L eq of 40 dB(A), as a function of the L eq of T. The linear regression function for T only and the annoyance caused by G only are given as references. In both panels (a) and (b), the solid line represents the prediction of the model with k = 16. (Source: Vos [121])

Figure 2 .

 2 Figure 2.1 illustrates roughness calculated over time for the stimulus dfo_4 using an RSL of 200 ms (a) and an RSL of 500 ms (b).

Figure 2 . 1 :

 21 Figure 2.1: Roughness over time of one powered-two-wheeler passing by at constant speed (Category 1). Roughness is computed using an RSL of 200 ms (a) and an RSL of 500 ms (b).

Figure 2 . 2 :

 22 Figure 2.2: Relationship between measured roughness (median values) and calculated roughness R 90 for 33 pass-by noises of all the seven perceptual categories reproduced at 60 dB(A). Calculated roughness values were obtained using the Oldenburg roughness model.

Figure 2 . 3 :

 23 Figure 2.3: Modulation spectra of the pass-by noises dao_2 (a) and dau_5 (b).

  Figure 2.3(a)) mainly consists of amplitude modulations between approximately 70 and 200 Hz. The pass-by noise dau_5 (Figure 2.3(b))

Figure 2 . 4 :

 24 Figure 2.4: Relationship between measured roughness (median values) and calculated roughness R mean for 33 pass-by noises of all the seven perceptual categories reproduced at 60 dB(A). Calculated roughness values were obtained using the dBSonic roughness model.

Figure 3 . 1 :

 31 Figure 3.1: Screenshot of the user interface showing the 25 scales in French language.

Figure 3 . 2 :

 32 Figure 3.2: The median values for the item "dull/shrill" plotted against the TETC values for the 14 urban road single-vehicle pass-by noises.

Figure 3 . 3 :

 33 Figure 3.3: The median values for the item "sputtering" plotted against the F max values for the 14 urban road single-vehicle pass-by noises.

Figure 3 . 4 :

 34 Figure 3.4: The median values for the item "nasal" plotted against the F 10 values for the 14 urban road single-vehicle pass-by noises.

Figure 3 . 5 :

 35 Figure 3.5: Modulation spectra determined over the total duration of the single pass-by noises. (a) noise dau_5 and (b) noise vao_3 are characterized as "sputtering"; (c) noise dfu_10 and (d) noise dao_2 are described as "nasal".

Figure 3 . 6 :

 36 Figure 3.6: The median values for the item "sputtering" plotted against the "sputtering" index values m sputt,10 for the 14 urban road single-vehicle pass-by noises.

10 Figure 3 . 7 :

 1037 Figure 3.7:The median values for the item "nasal" plotted against the "nasal" index values (m nas,10 ) for the 14 urban road single-vehicle pass-by noises.

  A. Klein, C. Marquis-Favre, R. Weber and A. Trollé, J. Acoust. Soc. Am., 137(3): 1238-1250, 2015 and L-A. Gille, C. Marquis-Favre and A. Klein currently in preparation for publication in a journal.

Figure 4 . 1 :

 41 Figure 4.1: Instructions in the French language as shown on the screen of the test unit.

Figure 4 . 2 :

 42 Figure 4.2: Screenshot of the testing unit: Reminder of the imaginary situation and the annoyance scale ranging from 0 ("not at all annoying") to 10 ("extremely annoying").

Figure 4 . 7 Figure 4 . 3 :

 4743 Figure 4.3 illustrates the mean scores obtained for each stimulus and their corresponding standard error indicated as vertical bars.

Figure 4 .

 4 4 illustrates the mean annoyance responses as a function of the TETC index values. As shown in Figure 4.4, both the heavy vehicle in deceleration pdo_6 and the powered-two-wheeler in deceleration ddu_2 exhibit the most energy in highfrequencies (cf. Figure A.5 and Figure A.15) and are well characterized by the TETC index.

Figure 4 . 4 :

 44 Figure 4.4: Mean annoyance responses plotted against the TETC values for the 33 pass-by noises equalized to 60 dB(A). The corresponding regression line is shown.

Figure 4 . 5 :

 45 Figure 4.5: Mean annoyance responses plotted against the values of the "sputtering" index (m sputt,10 ) (a) and the "nasal" index (m nas,10 ) (b), respectively, for the 33 urban road traffic noises equalized to 60 dB(A).

Figure 4 . 6 :

 46 Figure 4.6: Mean annoyance responses plotted against the F mean values (a) and the R max values (b), respectively, for the 33 urban road traffic noises equalized to 60 dB(A).

Figure 4 .

 4 Figure 4.7 illustrates the mean annoyance responses for the 33 stimuli.

7 Figure 4 . 7 :

 747 Figure 4.7: Mean annoyance responses for each urban road vehicle pass-by noise and their corresponding standard error (vertical bars). The perceptual categories (Cat.) from which the pass-by noises stem[START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF] are reported. In each perceptual category, the pass-by noises with the highest representation rating[START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF] is shown in red characters.

Figure 4 . 8 :

 48 Figure 4.8: Mean annoyance responses plotted against the N mean values for the 33 urban road vehicle pass-by noises with mean level differences measured in situ (cf. Section 4.2.2).

Figure 4 . 9 :

 49 Figure 4.9: Mean annoyance responses plotted against the TETC values for the 33 urban road vehicle pass-by noises with mean level differences measured in situ (cf. Section 4.2.2).

Figures 4. 10

 10 Figures 4.10(a), (b) depict the relationships between the mean annoyance responses and the values of the indices m sputt,10 (a) and m nas,10 (b).

  Figures 4.10(a), (b) depict the relationships between the mean annoyance responses and the values of the indices m sputt,10 (a) and m nas,10 (b).

Figure 4 . 10 :

 410 Figure 4.10: Mean annoyance responses plotted against the values of the indices characterizing modulation sensations for the 33 urban road single-vehicle pass-by noises. (a) m sputt,10 (r = 0.5; p < 0.01), (b) m nas,10 (r = 0.4; p < 0.05).

4. 4 . 3 . 4

 434 Assessment of the combination of the proposed indices: Indicator constructionThe calculation of annoyance by a suited combination of the indices was carried out using multiple linear regression. Based on significant correlation coefficients and partial correlation coefficients calculated between mean annoyance responses and indices, two regression models were created: the first model into which the indices N mean , m sputt,10 , m nas,10 and TETC are inserted (Model 1), and the second model into which the indices N mean , F 10 , and TETC are inserted (Model 2). If the R 95 index is included in Model 2, its regression coefficient is not significant, which is in line with the results regarding the partial correlations obtained in Section 4.4.3.3. Both models fit the mean annoyance responses well (Model 1: F(4,32) = 111.35, p < 0.001; Model 2: F(3,32) = 65.39, p < 0.001). The goodness-of-fit of each model was assessed using the determination coefficient (R 2 ), the adjusted determination coefficient (R 2 adj ), and the standard error of the estimate (SE). The two proposed models with their corresponding model fit and standard errors (SE) are presented in Table4.5 1 .

Table 4 . 5 :

 45 Multiple regression for noise annoyance calculation for the 33 pass-by noises with level differences. The model fit is represented by the parameter R 2 and R 2 adj . SE represents the standard error of each model. The regression equations shown in brackets are the models with the standardized regression coefficients. a p < 0.001, b p < 0.01, c p < 0.05. 0.50 b N mean + 2.85 b m sputt,10 + 3.51 b m nas,10 + 0.03 b TETC -0.79 0.94 0.93 0.35 (A c = 0.71 b N mean + 0.24 b m sputt,10 + 0.16 b m nas,10 + 0.22 b TETC -0.79) 2 A c = 0.51 b N mean + 0.02 c F 10 + 0.025 b TETC -0.13 0.87 0.85 0.50 (A c = 0.73 b N mean + 0.17 c F 10 + 0.22 b TETC -0.13)

Figure 5 . 1 :

 51 Figure 5.1: Mean measured partial annoyance due to road traffic noise as a function of the road traffic noises (a) and as a function of the tramway traffic noises (b). The standard error is presented along each mean value.

Figure 5 .

 5 Figure 5.1(a) indicates that both peak hour road traffic noises RT4[START_REF] Kaczmarek | Annoyance of time-varying road-traffic noise[END_REF] and RT5[START_REF] Kaku | Predicting overall reaction to multiple noise sources[END_REF] were judged to be more annoying than RT1[START_REF] Guski | The concept of noise annoyance: How international experts see it[END_REF], RT2[START_REF] Howell | Statistical Methods for Psychology[END_REF] and RT3[START_REF]Acoustics -Assessment of noise annoyance by means of social and socio-acoustic surveys[END_REF]. A post-hoc analysis (Tukey's HSD test) revealed that the road traffic partial annoyance responses due to these noises are significantly different from the partial annoyance responses due to other road traffic noises. At the same traffic density, the road traffic noises RT4[START_REF] Kaczmarek | Annoyance of time-varying road-traffic noise[END_REF] and RT5(53) comprising powered-two-wheelers, heavy vehicles and buses in addition to light vehicles were rated more annoying than road traffic noise RT2[START_REF] Howell | Statistical Methods for Psychology[END_REF] comprising only light vehicles. Despite the overlapping error bars of RT2[START_REF] Howell | Statistical Methods for Psychology[END_REF] and RT3(48) (cf. Figure5.1(a)), a post hoc test revealed that there is a small but significant difference between their road traffic partial annoyance responses. This is interesting as RT3(48) comprises particular annoying urban road vehicles, such as powered-two-wheelers, but in total only half the amount of vehicle pass-bys as RT2[START_REF] Howell | Statistical Methods for Psychology[END_REF] which is only composed of light vehicle pass-by noises. This indicates that the traffic composition may play an important role in annoyance due to road traffic noise. The road traffic noises RT4[START_REF] Kaczmarek | Annoyance of time-varying road-traffic noise[END_REF] and RT5[START_REF] Kaku | Predicting overall reaction to multiple noise sources[END_REF] were rated similarly annoying and are not significantly different. This suggests that an increase in the number of powered-two-wheelers (from 12 % in RT4(52) to 20 % in RT5(53)) for high traffic densities does not necessarily lead to an increase in annoyance.

Figure 5 .

 5 Figure 5.1(b) depicts the mean measured partial annoyance due to road traffic

Figure 5 . 2 :

 52 Figure 5.2: Mean measured partial annoyance due to tramway traffic noise as a function of the tramway traffic noises (a) and as a function of the road traffic noises (b). The standard error is presented along each mean value.

Figure 5 .

 5 3(a) illustrates the mean measured total annoyance responses for each combination of tramway and road traffic noises as a function of the road traffic noises.

Figure 5 . 3 :

 53 Figure 5.3: Mean measured total annoyance due to the combination of road traffic and tramway traffic noises (a) as a function of the road traffic noises and (b) as a function of the tramway traffic noises. The standard error is presented along each mean value.

Figure 5 .

 5 Figure 5.3(a) indicates that total annoyance due to the combination of peak hour road traffic noises RT4[START_REF] Kaczmarek | Annoyance of time-varying road-traffic noise[END_REF] and RT5[START_REF] Kaku | Predicting overall reaction to multiple noise sources[END_REF] with tramway traffic noises is higher. Post-

Figure 5 . 4 :

 54 Figure 5.4: Interaction between the factors RT and TT.

Figures 5. 5

 5 Figures 5.5(a) and (b) illustrate the mean measured total and partial annoyances due to peak hour road traffic noises RT2(46) and RT4(52) as a function of the SPL of the tramway noises. Both road traffic sequences are composed of the same number of pass-by noises but differ in their composition: RT2(46) comprises 34 light vehicles and RT4(52) comprises 4 powered-two-wheelers, 2 heavy vehicles and 2 buses in addition to 26 light vehicles.

  Figures 5.5(a) and (b) illustrate the mean measured total and partial annoyances due to peak hour road traffic noises RT2(46) and RT4(52) as a function of the SPL of the tramway noises. Both road traffic sequences are composed of the same number of pass-by noises but differ in their composition: RT2(46) comprises 34 light vehicles and RT4(52) comprises 4 powered-two-wheelers, 2 heavy vehicles and 2 buses in addition to 26 light vehicles.

Figure 5 . 5 :

 55 Figure 5.5: (a) : Mean measured total annoyance (A tot ) due to RT2(46)+T(X), as a function of the SPL of tramway noises; : Mean measured partial annoyance due to tramway noises (A part tramway). The linear regression line between A part tramway and the tramway SPL is calculated (A calc tramway); : Mean measured partial annoyance due to peak hour road traffic RT2(46) (A part RT2(46)). : Standard error (SE) of A part RT2(46) 2 . (b) : Mean measured total annoyance (A tot ) due to RT4(52)+T(X), as a function of the SPL of tramway noises; : Mean measured partial annoyance due to tramway noises (A part tramway). The linear regression line between A part tramway and the tramway SPL is calculated (A calc tramway); : Mean measured partial annoyance due to peak hour road traffic RT4(52) (A part RT4(52)) 2 .: Standard error (SE) of A part RT4[START_REF] Kaczmarek | Annoyance of time-varying road-traffic noise[END_REF]. The errorbars represent the standard error of A tot and A part tramway.

Figure 5 . 6 :

 56 Figure 5.6: Experimental set-up of the experimental living room.

Figure 5 . 7 :

 57 Figure 5.7: Loudspeaker set-up in front of the façade of the experimental cottage at the campus of the ENTPE.

Figure 5 . 8 :

 58 Figure 5.8: Mean measured partial annoyance due to road traffic noise as a function of the road traffic noises. The standard error is presented along each mean value.

  Both the factors TT and RT have a significant effect on tramway partial annoyance (respectively [F(3,99) = 40.83; p < 0.001] and [F(2.71,89.31) = 3.38 p < 0.05]). The proportion of variance explained by the factor TT is high, i.e. 84 % and by the factor RT very small, i.e. 1 %. No interaction between the factors could be observed [F(6.67,219.98) = 1.71; p = 0.11]. Figure 5.9 shows the mean measured partial annoyance due to tramway traffic noises as a function of each of the factors TT and RT.

Figure 5 . 9 :

 59 Figure 5.9: Mean measured partial annoyance due to tramway traffic noise as a function of the tramway traffic noises (a) and as a function of the road traffic noises (b). The standard error is presented along each mean value.

Figure 5 . 10 :

 510 Figure 5.10: Mean measured total annoyance due to combined road traffic and tramway traffic noises as a function of the road traffic noises (a) and as a function of the tramway traffic noises (b). The standard error is presented along each mean value.

Figures 5. 11

 11 Figures 5.11(a) and (b) illustrate total annoyance as a function of the SPL of the tramway traffic noises for the combinations RT1(48)+T(X) and RT4(58)+T(X).

  Figures 5.11(a) and (b) illustrate total annoyance as a function of the SPL of the tramway traffic noises for the combinations RT1(48)+T(X) and RT4(58)+T(X).

Figure 5 . 11 :

 511 Figure 5.11: (a) : Mean measured total annoyance (A tot ) due to RT1(48)+T(X), as a function of the SPL of tramway noises; : Mean measured partial annoyance due to tramway noises (A part tramway). The linear regression line between A part tramway and the tramway SPL is calculated (A calc tramway); : Mean measured partial annoyance due to off-peak hour road traffic RT1(48) (A part RT1(48)). : Standard error (SE) of A part RT1(48) 3 . (b) : Mean measured total annoyance (A tot ) due to RT4(58)+T(X), as a function of the SPL of tramway noises; : Mean measured partial annoyance due to tramway noises (A part tramway). The linear regression line between A part tramway and the tramway SPL is calculated (A calc tramway); : Mean measured partial annoyance due to peak hour road traffic RT4(58) (A part RT4(58)) 3 .: Standard error (SE) of A part RT4[START_REF] Kuwano | Subjective evaluation of car door sound[END_REF]. The errorbars represent the standard error of A tot and A part tramway.

  traffic sequences were created: • Off-peak hour road traffic combined with off-peak hour tramway traffic (R-4T) • Off-peak hour road traffic combined with peak hour tramway traffic (R-9T) • Peak hour road traffic combined with off-peak hour tramway traffic (R+4T) • Peak hour road traffic combined with peak hour tramway traffic (R+9T)

  noises [dB(A)]

Figure 5 . 12 :

 512 Figure 5.12: : Median total annoyance due to R-4T (A tot R-4T); : Median total annoyance due to R-9T (A tot R-9T); : Median partial annoyance due to off-peak hour tramway traffic (A part 4T (R-)); : Median partial annoyance due to peak hour tramway traffic (A part 9T (R-)); : Median partial annoyance due to off-peak hour road traffic measured in presence of peak hour tramway traffic (A part R-(9T)), with , its approximated confidence interval (CI); -: Median partial annoyance due to off-peak hour road traffic measured in presence of off-peak hour tramway traffic (A part R-(4T)), with , its approximated confidence interval (CI). The errorbars represent the approximated CIs of the median total annoyance and tramway partial annoyance ratings.

Figure 5 .

 5 Figure 5.13 illustrates total annoyance as a function of the SPL of the tramways (L p,4T , L p,9T ) for peak hour (R+) road traffic.

  noises [dB(A)]

Figure 5 . 13 :

 513 Figure 5.13: : Median total annoyance due to R+4T (A tot R+4T); : Median total annoyance due to R+9T (A tot R+9T); : Median partial annoyance due to off-peak hour tramway traffic (A part 4T (R+)); : Median partial annoyance due to peak hour tramway traffic (A part 9T (R+)); : Median partial annoyance due to peak hour road traffic measured in presence of peak hour tramway traffic (A part R+ (9T)), with , its approximated confidence interval (CI); -: Median partial annoyance due to peak hour road traffic measured in presence of off-peak hour tramway traffic (A part R+ (4T)), with , its approximated confidence interval (CI). The errorbars represent the approximated CIs of the median total annoyance and tramway partial annoyance ratings.

5. 5

 5 Discussion and comparison of the experiments 5.5.1 Partial annoyance responses

Figure 6 . 2 :

 62 Figure 6.2: Comparison between measured road traffic partial annoyances (A TT measured) obtained in Exp. A and calculated partial annoyance due to road traffic noise (A TT calculated).

  (a), (b) show the relationships between the measured tramway traffic partial annoyance responses as a function of the L Aeq values (a) and the measured tramway traffic partial annoyance responses as a function of the T ET C 12-24 values (b), respectively.

Figure 6 . 3 :

 63 Figure 6.3: Relationship between measured tramway partial annoyance and the L Aeq (a) and the T ET C 12-24 index (b).

Figure 6 .

 6 Figure 6.4 illustrates total annoyance responses predicted by the measured perceptual form of the strongest component model and the mixed model as a function of the measured total annoyance responses.

Figure 6 .

 6 Figure 6.5 shows the comparison of the predicted total annoyance responses using the calculated perceptual form of the strongest component model and the regression model as a function of the measured total annoyance responses.

Figure 6 . 4 :

 64 Figure 6.4: Total annoyance responses predicted using the measured perceptual form of the strongest component model ( , r = 0.99; p < 0.001) and the mixed model ( , r = 0.99; p < 0.001) as a function of the measured total annoyance responses.

Figure 6 . 5 :

 65 Figure 6.5: Total annoyance responses predicted by the calculated perceptual form of the strongest component model ( , r = 0.92; p < 0.001) and the regression model ( , r = 0.91; p < 0.001) as a function of the measured total annoyance responses.

Figure A. 1 :

 1 Figure A.1: Auditory spectrogram of the most representative pass-by noise of Category 1 (cf. [77]): dfo_4, a powered-two-wheeler at constant speed.

Figure A. 2 :

 2 Figure A.2: Auditory spectrogram of the most representative pass-by noise of Category 2 (cf. [77]): dao_2, a powered-two-wheeler in acceleration.

Figure A. 3 :

 3 Figure A.3: Auditory spectrogram of the most representative pass-by noise of Category 3 (cf. [77]): vfo_5, a light vehicle at constant speed.

Figure A. 4 :

 4 Figure A.4: Auditory spectrogram of the most representative pass-by noise of Category 4 (cf. [77]): ddo_1, powered-two-wheeler in deceleration.

Figure A. 5 :

 5 Figure A.5: Auditory spectrogram of the most representative pass-by noise of Category 5 (cf. [77]): pdo_6, heavy vehicle in deceleration.

Figure A. 6 :

 6 Figure A.6: Auditory spectrogram of the most representative pass-by noise of Category 6 (cf. [77]): vau_5, a light vehicle in acceleration.

Figure A. 7 :

 7 Figure A.7: Auditory spectrogram of the most representative pass-by noise of Category 7 (cf. [77]): pao_1, a heavy vehicle in acceleration.

Figure A. 8 :

 8 Figure A.8: Auditory spectrogram of dau_5, a powered-two-wheeler in acceleration.

Figure A. 9 :

 9 Figure A.9: Auditory spectrogram of ddu_1, a powered-two-wheeler in deceleration.

Figure A. 10 :

 10 Figure A.10: Auditory spectrogram of dfu_10, a powered-two-wheeler at constant speed.

Figure A. 11 :

 11 Figure A.11: Auditory spectrogram of pau_3, a heavy vehicle in acceleration.

Figure A. 12 :

 12 Figure A.12: Auditory spectrogram of vdo_1, a light vehicle in deceleration.

Figure A. 13 :

 13 Figure A.13: Auditory spectrogram of vao_3, a light vehicle in acceleration.

Figure A. 14 :

 14 Figure A.14: Auditory spectrogram of vfu_16, a light vehicle at constant speed.

Figure A. 15 :

 15 Figure A.15: Auditory spectrogram of ddu_2, a powered-two-wheeler in deceleration.

Figure A. 16 :

 16 Figure A.16: Auditory spectrogram of dao_3, a powered-two-wheeler in acceleration.

Figure A. 17 :

 17 Figure A.17: Auditory spectrogram of tramway pass-by noise used in T1.

Figure A. 18 :

 18 Figure A.18: Auditory spectrogram of tramway pass-by noise used in T2.

  Noise annoyance due to combined road traffic and tramway noises . . . . . . . . . . . . . . . . . . . . . . . . Items of the semantic differential . . . . . . . . . . . . . 3.2.5 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Dimensions of the perceptual space highlighted through the semantic differential . . . . . . . . . . . . . . . . . . . Descriptive terms related to temporal features of the noises . . . . . . . . . . . . . . . . . . . . . 3.3.3 Physical and perceptual characterization of auditory attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposal of indices characterizing modulation-related sensations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 "Sputtering" and "nasal" indices . . . . . . . . . . . . . . 3.4.2 Relationship between the proposed modulation indices and item judgments . . . . . . . . . . . . . . . . . . . . . .

	1.4.2 Contents 3.4	1.4.1.4 Combined noise sources paradox . . . . . . . . 3.3.2.2 3.3.3.1 Characterization of spectral noise features . . 3.3.3.2 Characterization of temporal noise features .	v

Introduction 3 1 Review of relevant literature 7 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2 Acoustical factors influencing noise annoyance . . . . . . . . . 8 1.2.1 Road traffic noise annoyance . . . . . . . . . . . . . . . . 8 1.2.2 Tramway noise annoyance . . . . . . . . . . . . . . . . . . 1.3 Non-acoustical factors influencing noise annoyance . . . . . . 1.4 Noise annoyance due to combined noise exposure . . . . . . . 1.4.1 Perceptual phenomena related to annoyance due to combined noise exposure . . . . . . . . . . . . . . . . . . 1.4.1.1 Strongest component . . . . . . . . . . . . . . . 1.4.1.2 Synergy . . . . . . . . . . . . . . . . . . . . . . . . 1.4.1.3 Inhibition . . . . . . . . . . . . . . . . . . . . . . . 1.4.3 Models for annoyance due to combined noise exposure 1.4.3.1 Weighted summation model . . . . . . . . . . . 1.4.3.2 Strongest component model . . . . . . . . . . . 1.4.3.3 Vector summation model . . . . . . . . . . . . . 1.4.3.4 Linear regression model . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Semantic differential test with verbalization task . . . . . . . . 3.2.1 Stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Apparatus . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.4 3.3.2 Perceptual features highlighted through the verbalization task . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2.1 Descriptive terms related to spectral features of the noises . . . . . . . . . . . . . . . . . . . . . 3.4.2.1 "Sputtering" index m sputt,10 . . . . . . . . . . . 3.4.2.2 "Nasal" index m nas,10 . . . . . . . . . . . . . . . . 3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Application of the relevant indices to different urban road vehicle pass-by noises 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Experimental methodology . . . . . . . . . . . . . . . . . . . . .

  The median values for the item "nasal" plotted against the "nasal" index values (m nas,10 ) for the 14 urban road single-vehicle pass-by noises. . . . 57 4.1 Instructions in the French language as shown on the screen of the test unit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.2 Screenshot of the testing unit: Reminder of the imaginary situation and the annoyance scale ranging from 0 ("not at all annoying") to 10 ("extremely annoying"). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.3 Mean annoyance responses for each urban road vehicle pass-by noise equalized in SPL and their corresponding standard error (vertical bars).The perceptual categories (Cat.) from which the pass-by noises stem

3.

[START_REF] Alayrac | Annoyance from industrial noise: indicators for a wide range of industrial sources[END_REF]

The median values for the item "sputtering" plotted against the "sputtering" index values m sputt,10 for the

[START_REF] Berglund | Guidelines for community noise[END_REF]

urban road single-vehicle pass-by noises. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 [77] are reported. In each perceptual category, the pass-by noise with the highest representation rating [77] is shown in red characters. . . . . 66 4.4 Mean annoyance responses plotted against the TETC values for the 33 pass-by noises equalized to 60 dB(A). The corresponding regression line is shown. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.5 Mean annoyance responses plotted against the values of the "sputtering" index (m sputt,10 ) (a) and the "nasal" index (m nas,10 ) (b), respectively, for the 33 urban road traffic noises equalized to 60 dB(A). . . . . . . . . . . . 70 4.6 Mean annoyance responses plotted against the F mean values (a) and the R max values (b), respectively, for the 33 urban road traffic noises equalized to 60 dB(A). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.7 Mean annoyance responses for each urban road vehicle pass-by noise and their corresponding standard error (vertical bars). The perceptual categories (Cat.) from which the pass-by noises stem [77] are reported. In each perceptual category, the pass-by noises with the highest representation rating [77] is shown in red characters. . . . . . . . . . . . . 73 4.8 Mean annoyance responses plotted against the N mean values for the 33 urban road vehicle pass-by noises with mean level differences measured in situ (cf. Section 4.2.2). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.9 Mean annoyance responses plotted against the TETC values for the 33 urban road vehicle pass-by noises with mean level differences measured in situ (cf. Section 4.2.2). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

  Comparison of correlation coefficients obtained from the characterization of annoyance using the spectral indices L LF , L MF , L HF calculated from the third octave spectrum between 25 and 250 Hz, 315 and 1250 Factor loadings obtained from the principal component analysis with varimax rotation for the 25 items. The four retained principle components explain 58 % of the variance. Items with factor loadings greater than or equal to 0.32 are highlighted in bold (cf.[START_REF] Tabachnick | Using Multivariate Statistics[END_REF]). . . . . . . . . . . 44 3.5 Results from the verbalization task "Can you describe why you give this annoyance rating?". Descriptive terms relating to spectral features which were used to explain annoyance ratings are provided. Their corresponding number of occurrences relative to the total number of terms referring to spectral features per stimulus is denoted in brackets. 46 3.6 Results from the verbalization task "Can you describe why you give this annoyance rating?". Descriptive terms relating to modulation sensations which were used to explain annoyance ratings are provided. Their Multiple regression for noise annoyance calculation for the 33 passby noises equalized to 60 dB(A). The model fit is represented by the determination coefficient R 2 and the adjusted determination coefficient R 2 adj . SE represents the standard error of each model. The regression equations shown in brackets are the models with the standardized regression coefficients. a p < 0.001, b p < 0.01, c p < 0.05. . . . . . . . . . 71 4.4 Bravais-Pearson correlation coefficients ( a p < 0.001, b p < 0.01, c p < 0.05) calculated between mean annoyance ratings and the acoustic/psychacoustic indices calculated for 33 pass-by noises with different sound pressure levels. Indices are computed accounting for the spectral characteristics of the noises (A-weighted sound pressure level in low (L LF ), mid (L MF ) and high frequencies (L

	2.1 Bark ranges derived from low-, mid-, and high-frequency ranges (cf. [6])
	01, c p < 0.05) calculated between mean annoyance ratings and the acoustic/psychacoustic 2.2 3.1 Urban road single-vehicle pass-by noises employed in the semantic indices calculated for 33 pass-by noises equalized to 60 dB(A). Indices
	differential test differing in vehicle type and driving condition. The are computed accounting for the spectral characteristics of the noises
	driving conditions are acceleration, deceleration and constant speed. . . (A-weighted sound pressure level in low (L LF ), mid (L MF ) and high frequen-
	cies (L

Hz, 1.6 and 12.5 kHz (cf. Morel

[START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF]

) and the values of the TETC index calculated in low-(1-2 Barks), mid-(3-10 Barks) and high-(11-24 Barks) frequency bands. The strongest correlation coefficients for each category are highlighted in bold characters. a p < 0.001, b p < 0.01, c p < 0.05. . .

2.3 Correlation coefficients (

a p < 0.001, b p < 0.01, c p < 0.05) between the mean annoyance responses per category and the values of V AP norm , V AP , ST DP norm and ST DP characterizing irregular amplitude variations. 2.4 Correlation coefficients ( a p < 0.001, b p < 0.01, c p < 0.05) between the mean annoyance responses per category and the values of seven indices calculated using the Oldenburg roughness model. . . . . . . . . . . . . . . 2.5 Correlation coefficients ( a p < 0.001, b p < 0.01, c p < 0.05) between the mean annoyance responses per category and the values of seven indices calculated using the dBSonic roughness model. . . . . . . . . . . . . . . . . 2.6 Spearman's rank correlation coefficients ( a p < 0.001, b p < 0.01, c p < 0.05) between the median values for the measured roughness and the values of seven indices calculated using the Oldenburg roughness model. 2.7 Spearman's rank correlation coefficients ( a p < 0.001, b p < 0.01, c p < 0.05) between the median values for the measured roughness and the values of seven indices calculated using the dBSonic roughness model. . 3.2 Groups of descriptive terms originating from the free verbalization task [75] in French language with their English translation. The number of occurrences of each term is given in brackets . . . . . . . . . . . . . . . . . 3.3 Eigenvalues and explained variance for the 4 principal components retained. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 corresponding number of occurrences relative to the total number of terms referring to modulation characteristics per stimulus is denoted in brackets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.7 Spearman's rank correlation coefficients ( a p < 0.001, b p < 0.01, c p < 0.05) between the median values for the item "dull/shrill" and "annoying" and the values of seven indices of sharpness S, of the A-weighted sound pressure level in high frequencies (L HF ) calculated from the third-octaveband spectrum between 1.6 kHz and 12.5 kHz, and of the Total Energy of the Tonal Components (TETC) within critical bands from 16 to 24 Barks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.8 Spearman's rank correlation coefficients ( a p < 0.001, b p < 0.01, c p < 0.05) between the median values for the items "sputtering", "nasal", "annoying", and seven values of roughness (R) and fluctuation strength (F ). 50 4.1 Level differences (∆ vfo ) between the average sound pressure levels measured in situ for the light vehicles at constant speed and the average sound pressure levels of other types of vehicles in different driving conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.2 Bravais-Pearson correlation coefficients ( a p < 0.001, b p < 0.HF ) (cf. [6]), sharpness S, Total Energy of the Tonal Components within critical bands from 16 to 24 Barks TETC) and the temporal characteristics of the noises linked to amplitude modulations (fluctuation strength F , roughness R, "sputtering" index m sputt,10 and "nasal" index m nas,10 ) The psychoacoustic indices were calculated using dBSonic 01dB-Metravib©. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.3 HF ) (cf. [6]), sharpness S, Total Energy of the Tonal Components within critical bands from 16 to 24 Barks TETC) and the temporal characteristics of the noises linked to amplitude modulations (fluctuation strength F , roughness R, "sputtering" index m sputt,10 and "nasal" index m nas,10 ) The psychoacoustic indices were calculated using dBSonic 01dB-Metravib©. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.5 Multiple regression for noise annoyance calculation for the 33 passby noises with level differences. The model fit is represented by the parameter R 2 and R 2 adj . SE represents the standard error of each model. The regression equations shown in brackets are the models with the standardized regression coefficients.

Table 2 . 1 :

 21 Bark ranges derived from low-, mid-, and high-frequency ranges (cf.[START_REF] Alayrac | Annoyance from industrial noise: indicators for a wide range of industrial sources[END_REF])

	Band Bark range
	LF	1 -2 Barks
	MF	3 -10 Barks
	HF	11 -24 Barks

Table 2 . 3 :

 23 Correlation coefficients ( a p < 0.001, b p < 0.01, c p < 0.05) between the mean annoyance responses per category and the values of V AP norm , V AP , ST DP norm and ST DP characterizing irregular amplitude variations.

	Cat. ∆L +	∆L -	∆N +	∆N -	V AP norm V AP	ST DP norm ST DP
	1	0.05	0.02	-0.48 a -0.48 a 0.85 a	0.86 a -0.01	0.91 a
	2	0.2	0.53 a 0.43 b	0.16	0.43 b	0.57 a -0.32	0.56 a
	3	0.07	0.12	0.69 c	-0.55 a 0.81 a	0.79 a -0.13	0.86 a
	4	0.01	0.22	0.69 a	0.06	0.24	0.38 c -0.15	0.48 a
	5	-0.05 0.01	0.56 a	-0.17	0.74 a	0.82 a -0.08	0.87 a
	6	0.33	0.34	0.88 a	-0.25	0.39 b	0.55 a -0.25	0.61 a
	7	-0.3	0.11	0.34 c	-0.29	0.49 a	0.66 a -0.2	0.69 a

Table 2 . 4 :

 24 Correlation coefficients ( a p < 0.001, b p < 0.01, c p < 0.05) between the mean annoyance responses per category and the values of seven indices calculated using the Oldenburg roughness model. 0.83 a 0.72 a 0.75 a 0.82 a 0.85 a 0.69 a 2 0.60 a 0.60 a 0.59 a 0.58 a 0.60 a 0.63 a 0.72 a 3 0.74 a 0.74 a 0.70 a 0.70 a 0.75 a 0.75 a 0.70 a 4 0.80 a 0.87 a 0.81 a 0.83 a 0.86 a 0.88 a 0.90 a 5 0.71 a 0.74 a 0.71 a 0.71 a 0.71 a 0.75 a 0.72 a 6 0.87 a 0.80 a 0.83 a 0.80 a 0.80 a 0.80 a 0.80 a 7 0.75 a 0.87 a 0.78 a 0.82 a 0.83 a 0.85 a 0.83 a

	Cat. R max	R mean R 5	R 10	R 50	R 90	R 95
	1	0.72

a

Table 2 . 5 :

 25 Correlation coefficients ( a p < 0.001, b p < 0.01, c p < 0.05) between the mean annoyance responses per category and the values of seven indices calculated using the dBSonic roughness model.

	Cat. R max	R mean R 5	R 10	R 50	R 90	R 95
	1	0.47 b 0.79 a 0.46 b 0.48 b 0.83 a 0.91 a 0.92 a
	2	0.54 b 0.53 b 0.66 a 0.68 a 0.38 c 0.61 a 0.65 a
	3	0.63 91 a
	6	-0.09	0.66 a 0.01	0.17	0.76 a 0.81 a 0.84 a
	7	0.49

a 0.85 a 0.65 a 0.63 a 0.84 a 0.92 a 0.92 a 4 0.48 c 0.82 a 0.56 b 0.85 a 0.85 a 0.69 a 0.66 a 5 0.61 a 0.90 a 0.63 a 0.78 a 0.90 a 0.92 a 0.b 0.91 a 0.77 a 0.78 a 0.85 a 0.76 a 0.76 a

Table 3 . 1 :

 31 Urban road single-vehicle pass-by noises employed in the semantic differential test differing in vehicle type and driving condition. The driving conditions are acceleration, deceleration and constant speed.

	Vehicle type	Driving	Acronym Duration (s)
		condition
	Powered-two-wheeler acceleration	dao_2	6.0
	Powered-two-wheeler acceleration	dau_5	5.6
	Powered-two-wheeler deceleration	ddo_1	5.4
	Powered-two-wheeler deceleration	ddu_1	3.7
	Powered-two-wheeler constant speed	dfo_4	4.0
	Powered-two-wheeler constant speed	dfu_10	4.4
	Heavy vehicle	acceleration	pao_1	6.4
	Heavy vehicle	acceleration	pau_3	5.5
	Heavy vehicle	deceleration	pdo_6	5.0
	Light vehicle	deceleration	vdo_1	5.3
	Light vehicle	acceleration	vao_3	5.9
	Light vehicle	acceleration	vau_5	4.7
	Light vehicle	constant speed	vfo_5	4.2
	Light vehicle	constant speed	vfu_16	4.5

Table 3 . 2 :

 32 Groups of descriptive terms originating from the free verbalization task[START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] in French language with their English translation. The number of occurrences of each term is given in brackets

	Groups	French adjectives English translation
	(a) Adjectives referring to	lisse (1)	smooth
	temporal characteristics	variable (4)	variable
		constant (5)	constant
		croissant (1)	ascending
		décroissant (1)	descending
		progressif (1)	progressive
		soudain (2)	sudden
		discontinu (4)	intermittent
		continu (4)	continuous
		doux (2)	soft
		saccadé (3)	staccato
		nasillard (1)	nasal
		pétaradant(3)	sputtering
		vrombissant (4)	humming
	(b) Adjectives referring to grave (25)	deep
	spectral characteristics	aigu (48)	sharp
		strident (5)	shrill
		sourd (6)	dull
		métallique (4)	metallic
		sifflant (6)	hissy
	(c) Adjectives referring to	fort (25)	loud
	intensity	faible (2)	soft
	(d) Adjectives referring to agréable (3)	pleasant
	hedonic judgments	désagréable (30)	unpleasant
		agressif

Table 3 . 3 :

 33 Eigenvalues and explained variance for the 4 principal components retained.

	PC Total	% of Variance Cumulative %
	1	8.352 33.407	33.407
	2	3.052 12.206	45.613
	3	1.660 6.642	52.255
	4	1.503 6.013	58.268

Table 3 . 4 :

 34 Factor loadings obtained from the principal component analysis with varimax rotation for the 25 items. The four retained principle components explain 58 % of the variance. Items with factor loadings greater than or equal to 0.32 are highlighted in bold (cf.[START_REF] Tabachnick | Using Multivariate Statistics[END_REF]).

	Items	PC 1	PC 2	PC 3	PC 4
		(33 %) (12 %)	(7 %)	(6 %)
	disturbing: not at all/extremely	.873	.204	.234	.073
	annoying: not at all/extremely	.867	.293	.220	.025
	pleasant/unpleasant	.864	.200	.156	.020
	stressful: not at all/extremely	.763	.173	.324	.096
	aggressive: not at all/extremely	.669	.295	.285	.256
	irritating: not at all/extremely	.652	.422	.187	.086
	loud/soft	-.421	-.236	.074	-.216
	staccato: not at all/extremely	.219	.798	.099	-.060
	sputtering: not at all/extremely	.219	.768	-.006	.178
	smooth/rough	.308	.745	-.026 -.067
	humming: not at all/extremely	.151	.739	-.103	.154
	intermittent/continuous	-.174	-.641	-.121	.178
	hard/soft	-.390	-.587	-.070 -.234
	variable/constant	-.251	-.421	-.209	.127
	bright: not at all/extremely	.094	.021	.809	.148
	hissy: not at all/extremely	.210	-.022	.736	-.270
	shrill/dull	-.315	.087	-.728	.011
	deep/sharp	.205	-.234	.680	.136
	metallic: not at all/extremely	.119	.399	.610	.056
	tonal: not at all/extremely	.162	.222	.553	.369
	slow/fast	.034	-.227	.027	.804
	dynamic: not at all/extremely	.270	.284	-.029	.577
	nasal: not at all/extremely	.122	.259	.401	.543
	progressive/sudden	.145	.081	.097	.111
	ascending/descending	-.087	-.022	-.039 -.128

Table 3 . 5 :

 35 Results from the verbalization task "Can you describe why you give this annoyance rating?". Descriptive terms relating to spectral features which were used to explain annoyance ratings are provided. Their corresponding number of occurrences relative to the total number of terms referring to spectral features per stimulus is denoted in brackets.

	Pass-by Descriptive terms used to explain annoyance ratings relating
	noise	to spectral features with number of occurrences
	dao_2	irregular frequency (1/5), sharp (2/5), high frequencies (2/5)
	dau_5	deep (1/2), rather dull (1/2)
	ddo_1	presence of high frequencies (1/7), dull (2/7), at the end shrill
		(1/7), bright (1/7), metallic (1/7), progressive frequency varia-
		tion (1/7)
	ddu_1	shrill (5/13), a little shrill (1/13), high frequencies (2/13), sharp
		(2/13), hissy (1/13), very hissy (1/13), high frequency hissing
		(1/13)
	dfo_4	too sharp (1/2), mid/highy frequencies (1/2)
	dfu_10	annoying frequencies (1/6), rather sharp (1/6), high frequen-
		cies (2/6), noise with loud high frequencies (1/6), rapid fre-
		quency variations (1/6)
	pao_1	deep (at the beginning) (1/8), sharp at the end (2/8), noise with
		increasing sharpness (1/8), more sharp (phase of acceleration)
		(1/8), bright (1/8), hissing (1/8), hissing of the engine at the
		end (1/8)
	pau_3	presence of sharp noise at the beginning of the sound (1/3) ,
		hissing (1/3), not very hissy (1/3)
	pdo_6	sharp noise (2/11), feel that my teeth will gnash with the brakes
		(1/11), presence of a shrill sound (1/11), hissing (2/11), strong
		increase of high frequencies during deceleration (1/11), sharp
		components (1/11), shrill (2/11), squealing (1/11)
	vdo_1	squealing of the breaks (1/5), the end shrill (1/5), dull at the
		beginning (1/5), shrill (1/5), large spectrum (1/5)
	vao_3	many low frequencies (1/4), getting more shrill (1/4), the end
		shrill (1/4), mid frequencies (1/4)
	vau_5	deep noise (1/2), mid frequencies (1/2)
	vfo_5	not shrill (1/2), mid/high frequencies (1/2)
	vfu_16	dull (1/3), shrill noise with high level (1/3), no variations of
		frequency components (1/3)

Table 3 . 6 :

 36 Results from the verbalization task "Can you describe why you give this annoyance rating?". Descriptive terms relating to modulation sensations which were used to explain annoyance ratings are provided. Their corresponding number of occurrences relative to the total number of terms referring to modulation characteristics per stimulus is denoted in brackets.

	Pass-by Descriptive terms used to explain annoyance ratings relating
	noise	to modulation characteristics with number of occurrences
	dao_2	nasal (6/10), sputtering (2/10), buzzing of a wasp (1/10), stac-
		cato (1/10)
	dau_5	sputtering (6/8), rough (1/8), low-frequency modulation (1/8)
	ddo_1	sputtering (1/5), modulation (1/5), rough (1/5), staccato (2/5)
	ddu_1	humming of the engine (1/3), rough at the beginning (1/3),
		beginning rather soft (1/3)
	dfo_4	humming (1/4), roughness (1/4), sputtering (1/4) modulations
		(1/4)
	dfu_10	nasal (5/7), noise of a buzzing wasp (1/7), smooth (1/7)
	pao_1	humming of the engine (1/4), sputtering at the beginning (1/4),
		staccato at the beginning (1/4), staccato (1/4)
	pau_3	change of speed (1/3), slightly staccato (1/3), soft (1/3)
	pdo_6	sputtering (2/3), different successive intervals including hum-
		ming (1/3)
	vdo_1	noise rougher at the end (1/8), very smooth at the beginning
		(1/8), humming at the end (1/8), rough (1/8), quite smooth at
		the beginning (1/8), smooth (1/8), staccato at the end (1/8),
		noise of the engine staccato (1/8)
	vao_3	noise which is slightly humming and sputtering (1/3), hum-
		ming noise (1/4), staccato noise of the engine (1/4) soft (1/4)
	vau_5	rough (2/3), humming (1/3)
	vfo_5	smooth (2/4), soft (2/4)
	vfu_16	soft (1/1)

Table 3 .

 3

		Dull/shrill Annoying
		(median)	(median)
	S max	0.58 c	0.52
	S mean	0.60 c	0.40
	S 5	0.74 b	0.57 c
	S 10	0.72 b	0.57 c
	S 50	0.58 c	0.41
	S 90	0.34	0.25
	S 95	0.37	0.36
	L HF	0.68 b	0.44
	T ET C	0.85 a	0.78 a

7: Spearman's rank correlation coefficients ( a p < 0.001, b p < 0.01, c p < 0.05) between the median values for the item "dull/shrill" and "annoying" and the values of seven indices of sharpness S, of the A-weighted sound pressure level in high frequencies (L HF ) calculated from the third-octave-band spectrum between 1.6 kHz and 12.5 kHz, and of the Total Energy of the Tonal Components (TETC) within critical bands from 16 to 24 Barks.

Table 3 . 8 :

 38 Spearman's rank correlation coefficients ( a p < 0.001, b p < 0.01, c p < 0.05) between the median values for the items "sputtering", "nasal", "annoying", and seven values of roughness (R) and fluctuation strength (F ).

		Sputtering Nasal	Annoying
		(median)	(median) (median)
	R max	0.27	-0.3	0.04
	R mean 0.45	-0.22	0.22
	R 5	0.31	-0.19	0.12
	R 10	0.27	-0.21	0.07
	R 50	0.22	-0.44	-0.06
	R 90	0.01	-0.58 c	-0.07
	R 95	0.01	-0.58 c	-0.07
	F max	0.63 c	0.71 b	0.46
	F mean	0.41	0.66 c	0.25
	F 5	0.62 c	0.68 b	0.46
	F 10	0.61 c	0.77 b	0.5
	F 50	0.27	0.48	0.25
	F 90	0.33	0.61 c	0.26
	F 95	0.26	0.60 c	0.25

Table 4 . 1 :

 41 .1. Level differences (∆ vfo ) between the average sound pressure levels measured in situ for the light vehicles at constant speed and the average sound pressure levels of other types of vehicles in different driving conditions

	Vehicle type	Driving condition Acronym ∆ vfo (dB(A))
	Bus	acceleration	bao	+9.1
	Bus	deceleration	bdo	+4.2
	Bus	constant speed	bfo	+7.5
	Powered-two-wheeler acceleration	dao	+7.2
	Powered-two-wheeler deceleration	ddo	+4.0
	Powered-two-wheeler constant speed	dfo	+5.3
	Heavy vehicle	acceleration	pdo	+9.1
	Heavy vehicle	deceleration	pao	+4.2
	Heavy vehicle	constant speed	pfo	+7.3
	Light vehicle	acceleration	vao	-2.4
	Light vehicle	deceleration	vdo	-4.5

Table 4 . 3 :

 43 Multiple regression for noise annoyance calculation for the 33 pass-by noises equalized to 60 dB(A). The model fit is represented by the determination coefficient R 2 and the adjusted determination coefficient R 2 adj . SE represents the standard error of each model. The regression equations shown in brackets are the models with the standardized regression coefficients. a p < 0.001, b p < 0.01, c p < 0.05. = 0.49 a m sputt,10 + 0.36 b m nas,10 + 0.38 b TETC + 2.08) 2 A c = 0.03 F mean + 0.01 c R max + 0.04 b TETC + 2.33 0.48 0.42 0.61 (A c = 0.22 F mean + 0.35 c R max + 0.42 b TETC + 2.33)

	Model
	No	Regression models for the calculated annoyance A c	R 2	R 2 adj	SE
	1	A

c = 3.5 a m sputt,10 + 4.75 b m nas,10 + 0.03 b TETC + 2.08 0.65 0.62 0.50 (A c

  have a large impact on the noise annoyance responses (cf. Figure4.7). In Table4.4, it can be seen that the loudness index N mean is strongly correlated with mean annoyance. The relationship between mean annoyance ratings and the N mean index values are illustrated in Figure4.8 by means of a scatter plot.

	Index	Unit	r
	L Amax L Aeq L A5 L A10 L A50 L A90 L A95 N max N mean N 5 N 10 N 50 N 90 N 95 L LF L MF L HF	dB(A) dB(A) dB(A) dB(A) dB(A) dB(A) dB(A) sone sone sone sone sone sone sone dB(A) dB(A) dB(A)	0.85 a 0.85 a 0.75 a 0.77 a 0.86 a 0.75 a 0.73 a 0.81 a 0.89 a 0.81 a 0.81 a 0.85 a 0.71 a 0.73 a 0.76 a 0.79 a 0.85 a
	S max	acum	0.24
	S mean	acum	0.16
	S 5	acum	0.18
	S 10	acum	0.15
	S 50	acum	0.16
	S 90	acum	0.14
	S 95	acum	0.12
	TETC	dB	0.55 b
	VAP		0.45 b
	V AP norm		0.47 b
	STDP		0.60 b
	F max F mean	cVacil cVacil	0.62 a 0.61 a
	F 5 F 10	cVacil cVacil	0.62 b 0.63 a
	F 50	cVacil	0.50 b
	F 90	cVacil	0.53 b
	F 95	cVacil	0.53 b
	R max R mean	cAsper cAsper	0.49 b 0.61 a
	R 5	cAsper	0.50 b
	R 10	cAsper	0.50 b
	R 50 R 90 R 95	cAsper cAsper cAsper	0.57 b 0.65 a 0.67 a
	m sputt,10 m nas,10		0.50 b 0.40 c

Table 4 .

 4 1, Section 4.2.2). Different examples of auditory spectrograms of pass-by noises used for the construction of the road traffic sequences are presented in Appendix A.1 to A.3.

  table using an artificial head. The stimuli are played back

	partially open window (~15 cm)
	coffee table	PC
	Experimental living room	Control room

  • N mean + 2.85 • m sputt,10 + 3.51 • m nas,10 Findings from Chapter 5 indicate that the number of pass-by noises could be taken into account to characterize total annoyance due to road traffic and tramway traffic. However, as the number of vehicle pass-bys per sequence is positively related to the L Aeq or N mean , a regression model comprising these two independent variables would not yield a strong model fit. Comparison between measured road traffic partial annoyances (A RT measured) obtained in Exp. A and calculated partial annoyance due to road traffic noise (A RT calculated).

		10
	Measured/calculated partial annoyance	2 3 4 5 6 7 8 9	A RT A RT	measured calculated
		1
	+ 0.026 • T ET C 16-24 -0.79 Figure 6.1 shows a comparison between measured and calculated partial annoyances (6.6) for the different urban road traffic noises studied in Exp. A (cf. Section 5.2). It is apparent that partial annoyance is generally well calculated. Correlation analysis between calculated and measured partial annoyance yields a strong positive correlation (r = 0.97; p < 0.001). The hypothesis that partial annoyance can be approximated by specific annoyance is valid. It should be highlighted that the indicator based on the different indices proposed in Chapter 4 is in good agreement with the measured road traffic partial annoyances obtained in Exp. A. This validates RT2(46) RT3(48) RT4(52) RT5(53) 0 Road traffic noises 1 RT1(43) Figure 6.1:

  for further details). This indicator was found to adequately explain short-term annoyance due to tramway pass-by noise in different operating configurations (straight line, in curve, arrival at station, departure from station). The authors proposed an indicator comprising L Aeq and the Total Energy of Tonal Components within critical bands from 12 to 24 Barks (here denoted T ET C 12-24 ) of the following form 2 :A TT = 0.158 • (L Aeq -46.1) + 0.148 • (T ET C 12-24 -46.1) + 1.98(6.7)

•

  Achim Klein, Catherine Marquis-Favre, Reinhard Weber, Arnaud Trollé. Spectral and modulation indices for annoyance-relevant features of urban road single-vehicle pass-by noises. Journal of the Acoustical Society of America, 137(3): 1238-1250, 2015. • Arnaud Trollé, Catherine Marquis-Favre, Achim Klein. Short-term annoyance due to tramway noise: determination of an acoustical indicator of annoyance via multilevel regression analysis. Acta Acustica united with Acustica , 100(1): 34-45, 2014.

•

  Achim Klein, Catherine Marquis-Favre, Reinhard Weber. How new temporal and spectral indices improve indicators of noise annoyance due to urban road traffic noise. In: Proceedings of Euronoise, Maastricht, Netherlands, 1639-1644, June, 2015 (Invited conference). • Achim Klein, Catherine Marquis-Favre, Reinhard Weber. Physical and perceptual characterization of modulation sensations for improving the assessment of noise annoyance due to urban road traffic noise. In: Proceedings of Internoise 2013, Innsbruck, Austria, p. 9, September, 2013 (Invited conference). SOUTENUE DEVANT L'ÉCOLE NATIONALE DES TRAVAUX PUBLICS DE L'ÉTAT NOM : KLEIN Date de soutenance : le 27 avril 2015 Prénoms : Achim TITRE : Indicateurs acoustiques caractéristiques de la gêne due au bruit routier urbain : seul, et en situation de multi-exposition en présence du bruit de tramway Nature : Doctorat Numéro d'ordre : 2015-ENTP-0002 École doctorale : Mécanique, Énergétique, Génie civil et Acoustique Spécialité : Acoustique Cote B.I.U.-Lyon :

• Arnaud Trollé, Catherine Marquis-Favre, Achim Klein. Acoustical indicator of noise annoyance due to tramway in in-curve operating configurations. In: Proceedings of Meetings on Acoustics, Montreal, Canada, p. 8, June, 2013. THÈSE

  l'École Nationale des Travaux Publics de l'État (ENTPE) Directrice de Thèse : Catherine MARQUIS-FAVRE Président du jury :

		Etienne PARIZET
	Composition du jury :	Patricia CHAMPELOVIER	Membre de jury
		Peter LERCHER	Rapporteur
		Catherine MARQUIS-FAVRE	Directrice de Thèse
		Anna PREIS	Rapporteur
		Reinhard WEBER	Membre de jury

In the following, the group of motorized two-wheeled vehicles, such as motorcycles, mopeds and scooters, will be referred to as powered-two-wheelers.

Referring to motorized two-wheeled vehicles, such as motorcycles, mopeds and scooters.

Due to the calculation interval of the Bark ranges imposed by dBSonic, the lower limit used for the calculation corresponds to x-0.98 and the upper limit to y-0.03.

The values of the correlation coefficients presented in Table2.2 may vary between the ones presented by Morel[START_REF] Morel | Caractérisation physique et perceptive du bruit routier urbain pour la définition d'indicateurs de gêne sonore en situation de mono-exposition et de multi-exposition en présence de bruit industriel[END_REF] as Morel used the left channel of the recordings for the calculation of the indices.

The experiment and analysis described in this chapter and in the article were carried out under the supervision of R. Weber and C. Marquis-Favre. A. Trollé assisted in designing the user interface for the experiment.

C.E Osgood, G. Suci & P. Tannenbaum, The measurement of meaning (Urbana, IL: University of Illinois Press, 1957) cited in Guski[START_REF] Guski | Psychological methods for evaluating sound quality and assessing acoustic information[END_REF]

The Matlab code for the calculation of the indices m sputt,10 and m nas,10 is available upon request.

The Psychoacoustic Annoyance index (PA) (see[START_REF] Fastl | Psychoacoustics -Facts and Models[END_REF], p. 328 for equation) has also been tested as an annoyance model for the current corpus of stimuli. It is based on the indices N 5 , R, F and S and it is thus close to Model

of this current work comprising N, F 10 and TETC. Linear regression analysis revealed a smaller model fit (R 2 adj = 0.61; SE = 0.81) compared to Model 1 and 2.

An indicator only based on L Aeq yields a determination coefficient of R 2 = 0.72. Just by replacing L Aeq with N mean , the determination coefficient increases up to R 2 = 0.79.

This notation refers to the arithmetic mean value from the partial annoyance ratings given for each road traffic noise or tramway noise five times (5 x 5 matrix). In the following "mean measured partial annoyance" will also be referred to as "partial annoyance".

To improve readability, the standard error of mean measured partial annoyance due to road traffic is only shown partially.

To improve readability, the standard error of mean measured partial annoyance due to road traffic is only shown partially.

The evaluated L Aeq values based on recordings of the sequences using an artificial head were 47 and 49 dB(A) for R-and R+, respectively. The estimated L Aeq values for 4T and 9T were

and 58 dB(A), respectively.

Approximated confidence intervals for the median (M) based on the asymptotic approximation of the standard deviation: M ± 1.7(1.25R/1.35 √ N) where R is the interquartile range and N the number of observations[START_REF] Mcgill | Variations of boxplots[END_REF].

Labelled Stimulus 40, 11 and 34 in Trollé and Marquis-Favre[START_REF] Trollé | Indicateurs acoustiques de la gêne en situation de mono-exposition au bruit de tramway[END_REF], respectively.

The coefficients obtained using multilevel regression analysis will be used for subsequent analyses in the current study. This approach can be found in literature (e.g.[START_REF] Miedema | Relationship between exposure to multiple noise sources and noise annoyance[END_REF]), using coefficients obtained from a multilevel regression model (e.g.[START_REF] Miedema | Annoyance from transportation noise: Relationships with exposure metrics dnl and denl and their confidence intervals[END_REF]).

MerciAdeAvotreAparticipation.A

Bibliography

noises [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . mized with an angle of α 1,2 = 105.9°and the calculated perceptual form with an angle of α 1,2 = 116.8°. Table 6.1 shows the regression equations obtained for the total annoyance models and their corresponding model fit using Exp. A. Table 6.1: Construction and comparison of the model fit of the selected total annoyance models in their measured perceptual (perc. meas), calculated perceptual (perc. calc.) and psychophysical (psy.) form. The standardized regression coefficients are shown in brackets. The model fit is represented by the determination coefficient R 2 , the adjusted determination coefficient R 2 adj and the standard error SE. r : the Bravais-Pearson correlation coefficient between the measured annoyance ratings and the calculated annoyance ratings. The regression equations shown in brackets are the models with the standardized regression coefficients. a p < 0.001, b p < 0.01, c p < 0.05. The selected models provide an overall strong model fit with the data of Experiment A. Regarding the measured perceptual total annoyance models, the best model fit can be obtained using the mixed model as the absolute difference term is significant and thus taking into account the synergistic effects present in Exp. A. The standardized regression coefficients in the regression equations indicate the contributions of each variable of the models to total annoyance. It can be seen that for the mixed model, the contribution of road traffic noise on total annoyance is, as expected, higher compared to tramway noise but the contribution of the tramway and of the interaction term are both significant with similar contribution.

Compared to the measured versions, the model fit of the calculated models is generally smaller due to cumulative error (at the partial annoyance calculation step and subsequently at the total annoyance calculation step). The vector summation model yields the smallest model fit compared to the other models. Regarding the strongest component and the regression model their R 2 adj and SE are almost equal. The linear regression model corresponds to the perceptual form of the psychophysical independent effects model. The independent effects model has not been tested in this work as the corresponding model fit was found to be weak for in situ annoyance data (cf. [START_REF] Pierrette | Noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF][START_REF] Pierrette | Corrigendum to noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison[END_REF]). For the calculated perceptual form of the mixed model, the coefficients are not significant as the interaction term is correlated with the calculated partial road traffic annoyance A RT and the calculated tramway traffic

Conclusions and perspectives

The aim of this thesis was to contribute to the improvement of noise annoyance assessment in urban areas. The focal point of this thesis in the first part was on the improvement of the physical and perceptual characterization of annoyance evoked by urban road single-vehicle pass-by noises with a focus on powered-two-wheelers. This vehicle type has been studied little in literature but is often considered the most annoying urban road vehicle pass-by noises by residents. The second part focused on the testing of the proposed indicator and the assessment of annoyance due to urban road traffic noise heard in presence of tramway traffic noise.

Summary of the principal findings

Chapter 2 tested indices and models from literature for improved characterization of annoyance due to urban road vehicle pass-by noises. The results were compared with the physical and perceptual characterization carried out per perceptual category of the perceptual and cognitive typology of urban road vehicle pass-by noises proposed by Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF]. The TETC index, tested in low-, mid-, and high-bark ranges, correlated strongly with mean annoyance responses per perceptual category. To test for improved characterization of irregular envelope variations, the variance of time-varying A-weighted Pressure normalized by the root-mean-square (RMS) A-weighted pressure (V AP norm index; denoted VAP index in [START_REF] Trollé | Short-term annoyance due to tramway noise: Determination of an acoustical indicator of annoyance via multilevel regression analysis[END_REF]) and two different versions of this index (non normalized VAP ; STDP, standard deviation of the time varying A-weighted pressure) proved highly relevant. The strongest correlation coefficients were obtained for the STDP index. It was shown that both indices should be considered for the characterization of annoyance due to road vehicle pass-by noises. Furthermore, to assess the performance of roughness models characterizing annoyance due to urban road vehicle pass-by noises, roughness values were calculated for each pass-by noise per perceptual category using i) a newly developed roughness model proposed by Oetjen et al. [START_REF] Oetjen | Rauigkeitsberechnung unter Berücksichtigung der Einhüllendenform[END_REF] taking into account the shape of the envelope, and ii) the roughness model implemented in dBSonic and based on Aures (cf. [START_REF] Aures | Ein Berechnungsverfahren der Rauhigkeit (A procedure for calculating auditory roughness)[END_REF]). First, for both models it could be observed that for perceptual categories comprising powered-two-wheelers, especially in acceleration (Category 2), smaller correlation coefficients between calculated roughness and mean annoyance responses were obtained compared to other categories. Generally, for the other categories strong correlation coefficients were obtained. Powered-twowheelers were found to exhibit specific noise characteristics related to amplitude modulations not covered by roughness and fluctuation strength. Through a small Appendix A

Auditory spectrograms of studied urban pass-by noises

In the following, Auditory spectrograms (ASPs) of urban pass-by noises [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF]