Alexander Sigman
email: lx.sigman@gmail.com

Nicolas Misdariis
email: nicolas.misdariis@ircam.fr

alarm/will/sound: Sound Design, Modelling, Perception and Composition Cross-Currents

An ongoing international arts-research-industry collaborative project focusing on the design and implementation of innovative car alarm systems, alarm/will/sound has a firm theoretical basis in theories of sound perception and classification of Pierre Schaeffer and the acousmatic tradition. In turn, the timbre perception, modelling and design components of this project have had a significant influence on a range of fixed media, electroacoustic, and media installation works realised in parallel to the experimental research. An examination of the multiple points of contact and cross-influence between auditory warning research and artistic practice forms the backbone of this paper, with an eye towards continued development in both the research and artistic domains of the project.

1 Research Residency programme, and is still in progress at the time of writing (April 2018).

Whilst the design of most sound-emitting components of the automobile (e.g. the audio system, engine, horn, turn signal, door and even Vehicle Sound for Pedestrians (VSP) in the case of electric vehicles [Misdariis and Cera 2017]) have continuously undergone improvements and refinements in recent decades, the car alarm has changed very little. Unlike other domestic and industrial security systems, audible automotive alarms have proven to be ineffective as deterrents, and are often considered more as an anti-social nuisance than as a benefit to the public.

INTRODUCTION

alarm/will/sound is a tripartite collaboration between a composer (Alexander Sigman), a research fellow in the field of sound perception and design (Nicolas Misdariis) and a product designer/visual artist based in Stuttgart and London (Matthias Megyeri). This project began in January 2013 under the auspices of the Ircam-IRC (Interface Recherche/Création) Artistic Taking as a point of departure this proven ineffectiveness and (largely for that reason) the relative lack of research into and development of audible car alarm design, we have sought to produce innovative modified alarm system prototypes, informed by musical, artistic, scientific and industry expertise, as well as sound perception research and acoustic modelling. In the larger scope, and to some degree, with reference to the work of [START_REF] Patterson | Extending the domain of auditory warning sounds⎯ Creative use of high frequencies and temporal asymmetry[END_REF], alarm/will/ sound can be seen as an attempt to extend the domain of auditory alarms, as it aims at developing an original framework and exploring new paradigms for proposing innovative morphologies and textures for warning signals. In addition, we consider the alarm system as a sort of NIME, triggered by a user's position in space, trajectory, and gestures. As such, a set of interaction design models have been proposed, in the interest of morphing the car alarm function from deterrence into engagement. These models are described in [START_REF] Misdariis | Innovative Sound Design of Car Alarms: A Case Study on Information Needs and Musical Creativity[END_REF].

In this paper, we discuss how the legacy of the timbre perception and classification models attributed to Pierre Schaeffer [START_REF] Schaeffer | Treatise on Musical Objects: An Essay Across Disciplines[END_REF][START_REF] Schaeffer | Treatise on Musical Objects: An Essay Across Disciplines[END_REF] and the acousmatic tradition have shaped our approaches to research in the area of auditory warnings. In turn, examples are provided as to how the sound taxonomy, modelling and geolocalisation dimensions of the project, as well as the results in two experiments on alarm sound causation identifiability and synthetic auditory warning attraction and repulsion levels (respectively) have been adapted to the composition of fixed media, electroacoustic and multimedia installation works. Finally, future artistic research directions for the project are proposed.

DESCRIPTION OF THE SONIC ENVIRONMENT

The first phase of the project entailed the elaboration and characterisation of a sound catalogue to apply to the modified car alarm prototypes. As is presented in Figure 1, the sound corpus taxonomy consists of three primary categories: Individual Sounds, Auditory Scenes, or sound complexes, and Real Car Alarm Sounds (i.e. the standard repertoire of six auditory warnings typical of audible car alarm systems). The Individual Sound category is divided into 1) Synthetic/Electroacoustic; 2) Vocal; 3) Film Danger Icons; and 4) Industrial/Mechanical Sounds. Further subdivisions were made primarily along semantic/contextual lines. One may observe from the figure that the Industrial/Mechanical rubric consists of the greatest number of taxonomical divisions, and is the most populated with sonic entries amongst the Individual Sound subcategories.

Among the non-synthetic sounds in all three primary categories, the majority were mined from existing sound databases (e.g. SoundIdeas, Blue Box, Auditory Lab and Freesound). Under 3 the Auditory Scenes rubric, a series of field recordings of public spaces in Paris⎯streets, the Forum Les Halles shopping concourse, the Pompidou Centre, metro stations and train car https://www.sound-ideas.com/, https://www.bestservice.com/sound_effects.html , http://www.auditory-lab.com/ interiors⎯were compiled in February 2013 by Alexander Sigman and Matthias Megyeri. It is intended that the collection of field recordings be expanded over time to include further sitespecific entries.

HOW DO WE LISTEN TO OUR SONIC ENVIRONMENT?

Different listening modes

A sonic environment can be defined, in a trivial way, by all the sounding objects or elements that belong to one's daily surroundings. Grounded on a more academic basis, this notion of sonic environment can be linked to the concept of soundscape developed by R.M. Schafer in his seminal book, The Tuning of the World (Schafer 1977). In this publication, Schafer makes a clear distinction between hi-fi and lo-fi soundscapes, the former obtaining a 'good' signal-to-noise ratio (SNR)-i.e. high enough to deliver an auditory perspective and make possible the distinction of several sources-and the latter being, on the contrary, a low SNR environment in which all sources seems to be mixed together and covered by background noise. Another way to define-and illustrate-the notion of sonic environment could also borrow from Nelken and de Cheveigne's definition of a general sound scene, linked to the functioning of auditory system in the brain: 'a skeleton of events on a bed of texture' [START_REF] Nelken | An ear for statistics[END_REF]. This last proposition converges towards the understanding of a sonic environment as a composition of singular sound sources and extended noises perceived more or less easily by human beings.

That being said, it has been well established that the perception of a sound signal coming from any source can be processed in differerent ways and at different levels of meaning. Initiating the musique concrète current, Pierre Schaeffer redefined the notion of listening by formalising four modes, each of them corresponding to a singular perceptual attitude:

• perceiving (ouïr) means listening to the sound without questioning, like an elementary, raw and passive perceptual action;

• hearing (entendre) means applying reduced listening, i.e. selecting in the sound signal some structural elements useful for the characterisation of the percept;

• listening (ecouter) means looking for cues and indices in the sound that will inform of its possible origin, its source and the nature of the event that has caused it;

• understanding (comprendre) means arriving at the meaning of the sound by considering it as a sign within an entire language.

A similar terminology was proposed by W. Gaver [START_REF] Gaver | What in the world do we hear?: An ecological approach to auditory event perception[END_REF] with regards to environmental sounds as defined by Vanderveer ('any possible audible acoustic event which is caused by motions in the ordinary human environment [...] Besides 1) having real events as their sources [...] 2) [they] are usually more "complex" than laboratory sinusoids [...] 3) [they] are meaningful, in the sense that they specify events in the environment [...] 4) the sounds to be considered are not part of a communication system, or communication sounds, they are taken in their literal rather than signal or symbolic interpretation.' [START_REF] Vanderveer | Ecological acoustics: Human perception of environmental sounds[END_REF]):

• musical listening focuses on sound properties;

• everyday listening focuses on the source that produced the sound.

The term musical is nevertheless possibly ambiguous because, as quoted by [START_REF] Tuuri | Same sound-different meanings: A novel scheme for modes of listening[END_REF], it may be possible to have a musical listening of a non-musical sound and, inversely, to focus on the sources (origins) of a musical signal⎯for instance, by trying to separate some instruments within the orchestra.

Combining theses notions, in his work dealing with image/sound relationships, Michel Chion defines three listening modes, or auditory perception attitudes, which constitute a sort of synthesis of Schaeffer and Gaver's approaches [START_REF] Chion | Audio-vision: sound on screen[END_REF]):

• reduced listening, taken from Schaeffer's terminology;

• causal listening, similar to Gaver's 'everyday listening';

• semantic listening, related to the meaning of sound.

Since that time, some scientific studies have aimed at demonstrating⎯even partially⎯the mental structure associated with the perception of environmental sounds that has been established more or less empirically by their respective authors. In particular, within the framework of the European collaborative research project CLOSED (Closing the Loop of Sound Evaluation and Design; [START_REF] Susini | Closing the loop of sound evaluation and design[END_REF], Lemaitre and colleagues (2010) have collected and studied a sound corpus related to a specific daily environment⎯the kitchen⎯and have experimentally discovered two main listening levels, similar to those defined by Chion:

• acoustic level: analytic listening of the abstract properties of a sound;

• causal level: listening of the sound production phenomena.

Moreover, this study also showed the interaction of two main factors on these listening modes:

• identifiability (i.e. the ability of the sound to be identified by the listener): the less the sound is identifiable, the more the acoustic listening mode is involved;

• expertise: the more the listener is inexperienced, the more the causal listening mode is involved.

Warning sounds

In the specific case of warning sounds, these different ways of interpreting the auditory perception mechanisms can be found again through different formal approaches and methodologies for designing auditory warnings. Simply sketched, on the one hand, [START_REF] Patterson | Guidelines for auditory warning systems on civil aircraft[END_REF] proposed a structure for alarms based on a hierarchical decomposition of the signal into elementary components (from shortest to longest): pulses, bursts and warnings. A pulse is defined as an element of noise or vowel-like signal of a few milliseconds in length, a burst consists of multiple pulses, and a warning of multiple bursts. In constructing this taxonomy, Gaver attempts (with limited success) to bridge the gap between the atomic level and the semantic level of an auditory warning. Despite its reductive nature, Gaver's formalisation within the framework of auditory icons [START_REF] Gaver | Auditory icons: Using sound in computer interfaces[END_REF]) led to a recommendation to use environmental sounds for warnings (or at least auditory displays) able to be easily understood and associated with a particular object or context. A third way to consider the development of warning sets, in reference to [START_REF] Stanton | Auditory affordances in the intensive treatment unit[END_REF] formalisation, rests upon a user-centred approach and the notion of auditory affordances.

This latter notion was borrowed by the authors from Gibson's theory of affordances, tied to the ecological approach that he advocates to explain perceptual mechanisms (Gibson 1979). This theory provides a precise specification of the objects of sensory stimulation that are available for the human organism. According to this theory, perception is active, linked to movement, and picks up the variations and structural stability of a flux of information received by the sensory organs. In contrast to top-down models of perception, the idea of an internal mental representation is alien to Gibson's, as information is already contained in the structure of the sensation (for instance, of light energy or sound timbre). The perceptual mechanisms are thus focused on the perceived object and its intrinsic data (data-driven), thereby obviating the need to process information coming from the sensory modalities for building a representation of the world.

To paraphrase Gibson, the notion of affordance can be defined as the potentially useful and relevant information for a person to take action (Gibson 1977). Moreover, Stanton and 4 Edworthy have extended and applied this notion to the perception of auditory warnings, by proposing a 'theory of auditory affordances', and consequently a methodology for designing auditory warnings, that assumes that 'people understand sounds in the world in terms of their potential for action.' (Stanton and Edworthy 1998: 393-4). For instance, in an Intensive Treatment Unit (ITU) medical context, the sound from a syringe pump should confer the action of replacing the drug (Ibid: 392). More precisely, the associated methodology and the corresponding pilot study described by the authors examined and tested several options of sound design, ranging from nomic to symbolic, including metaphoric propositions for new auditory warnings in an ITU setting (for instance, heartbeats for cardiac monitoring, music for an infant warmer device or bubble sounds for syringe pump) (Ibid.) [START_REF] Schalkwijk | Alarm and Auditory-Interface Design: Learnability of alarms and auditory-feedback for random and meaningful alarm sounds investigated in a paired-associate paradigm[END_REF] addresses associations between two classes of auditory warnings and their respective vehicle and aviation referents. The two classes of auditory warnings are identified as follows:

• auditory icons: iconic representations (or caricatures) of sounds occuring in everyday life situations and, again, transposed in virtual environments to embody objects they contain or actions they suggest [START_REF] Gaver | Auditory icons: Using sound in computer interfaces[END_REF];

Also interesting to note that this notion has been re-contextualised in the ergonomics field, as a characteristic of

• abstract melodic alarm signals (i.e. earcons): arbitrary sounds linked to elementary actions in a virtual environment, and in some cases, using hierarchical rules for creating complex and composite messages [START_REF] Blattner | Earcons and icons: Their structure and common design principles[END_REF].

As such, the efficacy of music-element-based metaphorical alarms is systematically examined to a greater extent than has been the case in previous studies. The issue of prior associations/mental models of subjects for the presented abstract melodic alarm stimuli did emerge when conducting experiments. However, the demonstrated ability to form such associations that are not easily verbalised by subjects suggests their utility in improving non-verbal communication in humancomputer interfaces (HCI).5

The formal approaches described above fit quite clearly into the scope of the alarm/will/ sound project, especially by considering the way in which new synthetic auditory warnings may induce human actions/reactions against the sounding object (the vehicle, in this case).

Iconic vs. abstract sounds: sound causation identifiability experiment (#1)

The main objective of this first perceptual experiment was to determine levels of sound source identifiability amongst selected items of the sound catalogue (see the Description of the Sonic Environment section above and Figure 1). Given the size and scope of the catalogue, only a subset of the Industrial/Mechanical category was chosen as stimuli. This category was preferentially selected because of its large number of sub-categories and the varying levels of source abstractness relative to the other Individual Sound categories. The expected result of this experiment was to assume the form of an abstractness-iconicity scale constructed across the corpus that would also help to further determine salient semantic and acoustical attributes of the sounds (see Section 4). Such a scale could be formally related to one of the the two fundamental concepts of earcons and auditory icons, as defined in Section 3.2.

The experimental framework was an online test in which subjects were asked to listen to each stimulus, provide a brief description of sound causality and indicate a confidence rating of causality identification on a standardised five-point Likert scale (see Figure 2 for a depiction of the experimental user interface).

However, this study does not investigate which musical parameters best map onto which respective referent Figure 3 presents the confidence ratings of the 39 tested stimuli, averaged over the 15 usable participant data sets and listed in increasing order (from low to high). After careful analysis of the (useable) data, it became evident that there were no instances of a high confidence level being correlated with an incorrect or vague sound identification, and vice versa. Given this finding, it was determined that, for the purposes of this experiment, confidence rating was a viable measure of sound iconicity. As such, a significant difference t-test was made between the response vector associated with each stimulus and those of the highest-and lowest-ranked sounds. This enabled the authors to localise the points along the confidence rating scale at which the difference between the upper-bound and i th sound, and symmetrically, the i th and lower-bound sound became significant. Fortunately, these points were identical. It was therefore possible to demarcate the threshold between iconic (strongly identifiable) and non-iconic sounds (represented by the thick vertical line in Figure 3). An analytic view of Figure 3 can lead to the following assumptions: the sounds that caused the most confusion have either contextually-obscure sources (wooden gears or milling machine), impulsive features (grenade blast, marbles in vase, or a ball-ricochet) or are steadystate and drone-like in nature, with motor sources (air conditioner, microwave oven, boat motor or food processor). By contrast, the iconic sounds are quite context-specific (grandfather clock bells, antique train or machine gun), time-varying (e.g. helicopter, airplane or train passing), or common (e.g. corkscrew opening a bottle or alarm clock sounding).

The results of Experiment #1 in alarm/will/sound project allowed the authors⎯as expected⎯to situate the sound stimuli along an abstractness-iconicity gradient and to determine salient semantic characteristics of each sound of the corpus. It was then concluded that sounds falling to the right of the iconicity threshold (see Figure 3 above) would not fit the auditory warning context, being potentially too closely attached to specific sources that may exist in the vehicle's direct environment (e.g. airplane, train, etc.). The definition of this threshold also allowed the authors to define an operational sound corpus for the subsequent phases of the project.

SOUND MODELLING AND DESIGN

The concept of sound modeling is closely connected to the development of tools and methods in the signal processing domain that allow to analyse and describe the content of a sound signal. In some cases, these developments have been based on previous seminal works achieved, since the 1970s, in the domain of characterisation of musical timbre. Among the most cited, [START_REF] Grey | Multidimensional Perceptual Scaling of Musical Timbres[END_REF], [START_REF] Wessel | Timbre Space as a Musical Control Structure[END_REF], [START_REF] Krumhansl | Why is musical timbre so hard to understand[END_REF] and McAdams and colleagues (1995) have studied the subjective attributes underlying the perception of a specific musical sound corpus (synthesised or recorded, percussive or sustained, etc.) and have tried to find acoustic correlates with some⎯or a combination of⎯objective parameters computed from the signal (see [START_REF] Mcadams | Musical timbre perception[END_REF]] for an updated and comprehensive review on that question).

More recently, this fundamental approach has been transposed to environmental⎯i.e. non-musical and non-speech⎯sounds in order to evaluate others types of sounds, upon the hypothesis that the notion of timbre could be applied to extended signals potentially encompassing a large frequency band, or containing very noisy components or a complex array of temporal profiles. Following on from the inital studies on musical timbre (mentioned above), and aiming at a larger 'universality' for a model of environmental sound perception, a metaanalysis was recently undertaken on several studies related to industrial sound stimuli [START_REF] Misdariis | Environmental sound perception: Metadescription and modeling based on independent primary studies[END_REF]. The meta-analysis revealed a two-level perceptual structure: on one level, a categorical clustering of sounds, plotted along continuous acoustic dimensions within each category; on the other, an inter-category plotting of sounds with respect to acoustic features shared across categorical boundaries. This confirms the hypothesis of common acoustic features for different kinds of sounds.

Acoustic description of the sound corpus

On the basis of specific acoustic parameters that had been discovered in the [START_REF] Misdariis | Environmental sound perception: Metadescription and modeling based on independent primary studies[END_REF] study to be critical to an efficient description of heterogeneous sets of environmental sounds, the sound corpus used in the alarm/will/sound project⎯and more precisely, the subset of thirty-one Industrial/Mechanical sounds tested in Experiment #1 that fell below the iconicity threshold⎯has been characterised in a two-dimensional physical space, where each dimension, representing a specific acoustic feature, has been demonstrated to be significant in the perception of environmental sounds.

The two features defining this physical space are perceptual spectral centroid (PSC) and harmonic/noise energy ratio (HNR). These parameters were computed in the Timbre Toolbox framework by means of IrcamDescriptor v2.7 routines (Peeters 2003). PSC and HNR are defined as follows (see [START_REF] Misdariis | Environmental sound perception: Metadescription and modeling based on independent primary studies[END_REF] for details):

• PSC (perceptual spectral centroid): amplitude-weighted arithmetic frequency average of the signal spectrum, computed on each channel of a gammatone filterbank based on the ERB (equivalent rectangular bandwidth)-rate scale [START_REF] Slaney | Auditory toolbox[END_REF]).

• HNR (harmonic/noise energy ratio): relative amount of harmonic energy and noise energy contained in the signal. Once both parts (harmonic/noise) of the signal are extracted, the feature simply consists of the ratio of their respective sound levels, i.e. energy computed with regards to the ERB-rate scale, as above.

Ideally, a control experiment consisting of a dissimilarity listening test would have been useful to verify the coherence between the a priori physical space and the actual perceptual space. In other words, this control test would assess the fact that the perceptual dimensions underlying this sound corpus can actually be explained by the chosen physical parameters PSC and HNR. At this stage of the process, the authors assume, on the basis of Misdariis et al.'s meta-analysis conclusions and the comparable nature of the sound stimuli used in the 2010 study, that these features are significatively relevant regarding the perception of the selected sound corpus.

Populating the physical space: source, hybrid and reference sounds

After this computational stage, the subset of 31 stimuli, obtained by considering the iconicity threshold from Experiment #1 on the initial 39-sound corpus, could be situated inside the PSC-HNR physical space, as depicted in Figure 4. In order to fill empty or under-represented areas of this physical space determined by the two acoustic features, hybrid sounds were constructed via applying constant cross-synthesis and LPC amplitude and phase source-filter synthesis algorithms to pairs of one-second samples of extant sounds in AudioSculpt. Decisions for each sample pair regarding which synthesis algorithm to employ, as well as constant cross and sourcefilter analysis settings (e.g. LPC order, in the case of source-filter synthesis) were made based upon analyses of the combined signals, as well as a process of trial-and-error. PSC and HNR values for candidate hybrid out-signals were computed using the methodology described above.

Those synthesised sounds whose PSC or HNR values fell roughly half-way between the values of the input signals from which they were generated were retained and integrated into the acoustic features space. Given that the objective of this procedure was merely to populate gaps in the descriptor space across the PSC range, and within the band of HNR ≤ 1.5 in which the vast majority of source sounds fell, it was neither necessary nor productive to apply stricter hybrid selection criteria.

Finally, for sake of homogeneity and curiosity, a subset of real car alarms collected during the initial step of constitution of the sound catalogue were also added to the physical space so as to be able to compare existing textures (timbres) used in current auditory warnings with the textures proposed and realised within the alarm/will/sound project. This comparison can be considered as the first step towards the innovative design of car alarms targeted by the project, and can be used as a first informal representation of the authors' ability to extend the current domain of auditory warnings. After having preferentially focused in the previous step on the notion of texture (see above) via the timbre characterisation of a selected set of sounds within the initial corpus, the work was then continued by considering more precisely the temporal shape of the new warnings. This approach was centred around attending to two high-level sound parameters that could be considered musical, to a certain extent: 1) the temporal morphology of the energy and 2) the rhythm, in the form of a very specific attribute for alarms, the inter-onset interval (IOI). Finally, the combination of source texture, energy profile and rhythm/rate lead to the construction of an original corpus of new types of alarms which were evaluated and assessed in a second sound perception experiment (Experiment #2: see below).

Temporal Morphologies of Environmental Sounds

Regarding the question of temporal morphologies, the authors' point of departure was Peeters and Deruty's study of an (automatic) morphological description of sounds [START_REF] Peeters | Automatic morphological description of sounds[END_REF], that has been thereafter perceptually reinforced and augmented thanks to an original protocol based on subjects' graphical production on a WACOM Intuos tablet [START_REF] Minard | Catégorisation des sons environnementaux sur la base de profils morphologiques. 10ème Congrès Français d'Acoustique[END_REF]). In the latter study, 55 sound stimuli of varying duration were presented. 12 sounds ranged between 350 ms.-1 second, 18 were between 1 and 3 seconds, and 25 between 3 and 5.15 seconds. These two studies formalise a taxonomy of six perceptually distinct environmental sound morphologies: stable, decreasing, increasing, single impulse, pulse-train and rolling, whose symbols⎯extracted from an 'average profile' of all subjects' drawings in the Minard et al. study⎯are depicted in Figure 5. In the context of alarm/will/sound, these six specific energy envelopes have then been modelled as BPFs (break-point functions) and have been used as amplitude modulation envelopes to create 186 (6x31) specimens of alarms combining different textures (timbre) and temporal profiles.

Existing Car Alarm Inter-Onset Intervals (IOIs)

The inter-onset interval (IOI) feature has been theorised, among others, by Patterson in the context of proposing guidelines for the creation of auditory warnings. In so doing, Patterson defines a standard structure of an alarm: a series of bursts made of a series of noise pulses [START_REF] Patterson | Guidelines for auditory warning systems on civil aircraft[END_REF]. The time between the bursts in the series is identified as the inter-onset interval, a leading attribute of an auditory warning, responsible for creating a sense of perceived urgency (Sueid, [START_REF] Suied | Evaluating Warning Sound Urgency with Reaction Times[END_REF].

In order to explore this fundamental feature, the inter-onset intervals of six standard car alarms,derived from the Car Alarm Prototypes category of the sound corpus depicted in Figure 1, and situated in the acoustic features space (Figure 4), were calculated in the Matlab 7 environment. For each of them, an elementary component⎯the 'burst' in Patterson's formulation⎯was manually isolated and used to detect the periodicity of the alarm that directly provides the IOI value (see Figure 6 for a depiction of this principle). Given the similarity in IOI of alarms 1 and 2, 3 and 5, and 4 and 6 (respectively) to each other, each pair of IOIs was averaged, producing three distinct IOI mean durations: 300 ms (named 'short'), 536 ms (named 'medium') and 2082 ms (named 'long'). These three different IOI values were used to create 558 (3x6x31) specimens of alarms combined with the previous combination set of different textures (timbre) and temporal profiles.

REPULSION-INDIFFERENCE-ATTRACTION TO SYNTHETIC AUDITORY WARNINGS (EXPERIMENT #2)

The objective of this experiment was to determine levels of repulsion, indifference and attraction to a set of synthetic auditory warnings, constructed from original and hybrid (cross-synthesised) sounds situated in the acoustic feature timbre space (Spectral Centroid/Harmonic-to-Noise Ratio), deploying typical temporal morphologies of environmental sounds and IOI values of real car alarm sounds, as described above. This parametric approach would thereby make it possible to distinguish between synthetic stimuli-and signal parameters-that would be more suitable for serving a deterrence function, and those that would better lend themselves to providing engaging auditory feedback (e.g., in the context of exploring an alarm system interface).

In the interest of limiting the required completion time for the experiment to 15-20 minutes, 45 synthetic auditory warnings, rather than all possible permutations of sound sources, temporal morphologies and IOIs described above were employed as stimuli. These stimuli were selected on the basis of contrast of spectral and temporal characteristics. Ultimately, five sound sources-three original sounds and two hybrids-were chosen as follows (indices correspond to position in the timbre space depicted in Figure 4): 1) air conditioner being switched on (02) ; 2) hybrid between a toppled metal can and low frequency components of an electric screwdriver sample (06H); 3) hybrid between a microwave oven being activated and a foghorn (08H) 4) fog horn (12); and 5) stationary part of a shaver sample, between onset and offset (27). These five samples were combined with three temporal morphologies (increasing, decreasing and single impulse) and the three inter-onset intervals.

Subjects were directed to the listening test URL, and asked to read a set of instructions 8 (in English or French) prior to beginning the trials. In the instructions, it was recommended that http://recherche.ircam.fr/equipes/pds/projects/asigman/deterrence/src/EvaluationStart.php subjects, when categorising their reaction to each sound stimulus into one of three types ('repulsion', 'indifference' or 'attraction'), imagine hearing the presented stimuli in a realistic context/interactive situation. The concepts of repulsion/indifference/attraction to a given sound stimulus were illustrated via examples from the visual and olfactory domains (e.g. a blinding light or unbearable odour vs. an enticing or intriguing light or odour). Additionally, it was explained that a 'repulsion' reaction would induce the subject to flee the source of the stimulus, whilst an 'attraction' rating would be predicated by approaching/expressing interest in the sound source. A sound with an 'indifference' rating would not have any major impact on one's current behaviour. Subjects were advised that it was not necessary to listen to each sound in its entirety prior to providing a response.

The 45 stimuli were presented to the subjects in random order. Playback was triggered via an embedded audio player (in a manner identical to Experiment #1). Stimuli could be repeated any number of times, and stopped at any point. Advancing to the next trial was only possible after selecting one of the three reaction categories for the current stimulus. For each trial, an optional comments field was provided. In order to avoid auditory fatigue, the experiment was divided into three 15-trial stages.

A detailed report and discussion of the results obtained from Experiment #2 are documented in [Misdariis and Sigman 2016: 185-194]. For the sake of brevity, a summary of the salient findings is provided here. In terms of experimental design, this second experiment measured a deterrence judgment that could be assigned three nominal modalities ('repulsion', 'indifference' and 'attraction'). The variation of this dependent variable was examined with respect to three independent controlled factors that could each be assigned to different discrete modalities: the nature of sound source (texture), the signal morphology and the duration of the IOI that structures the auditory warning. The raw data collected was arranged in an Nxm matrix, N being the number of sounds (N=45) and m being the number of modalities (settings) of the measured variable (m=3). This matrix was filled with the number of occurrences added up among all the participants, for each cell Sound/Variable. The sum of each line of this matrix corresponds then to the total number of participants. Moreover, from this initial data set, three aggregated matrices were also compiled with respect to the three experimental factors: a five-bythree matrix gathering the occurrences of repulsion/indifference/attraction for the five sound sources, and similarly, two three-by-three matrices related to the two other factors (morphology and IOI duration, respectively).

If one examines the test results by experimental factors (source, morphology and IOI), it becomes clear from Figures 789that while IOI duration seemed to have a direct impact on attraction/indifference/repulsion rating, the influences of morphology and sound source differences are less clear. With regards to morphology (Figure 8), the single impulse envelope correlated with slightly lower repulsion ratings than the increasing and decreasing envelopes. As for IOI duration (Figure 9), medium and short IOIs were associated with higher repulsion levels than long IOIs. A deeper numerical analysis was made by means of a Chi 2 statistical test. First, a general test of this kind made on the aggregated matrices of data conditioned as mentioned above, allowed the rejection of a null hypothesis of independence between the measured variable (deterrence) and the experimental factors: source, morphology and IOI duration. In fact, for every configuration, the computed Chi 2 value was significantly higher than the critical Chi 2 value given by Pearson's Law for a standard 5% margin of error (and even for the lowest 0.5% risk 9 rate). A second stage of analysis, using a one-way test, allowed us to examine more precisely this same null hypothesis for each vector of observations, i.e. each modality of the measured variable. The computed Chi 2 values were once again higher than the critical one given by Pearson's Law, with the exception of two conditions corresponding to the conservation of the null hypothesis (H0): no significant relationships between either Morphology and Attraction or IOI duration and Indifference assessment could be inferred. (See As is illustrated in Figure 10, it became apparent that too few stimuli received sufficiently high attraction scores to determine a threshold between indifference and attraction. However, the repulsion levels and quantities of stimuli associated with elevated repulsion levels, combined with the verbal reporting of subjects, will enable the authors to construct a repulsion gradient (spanning 'neutral' to 'extremely repulsive'), and situate the stimuli along this gradient accordingly. While based on the data obtained it would be a speculative exercise to attempt to segment the synthetic auditory warnings into 'attractive' and 'repulsive' categories, it would be possible to filter and select the best deterrence candidates. On a deeper level, this study has confirmed the salient role that IOI duration plays in auditory warning design, and has enabled the authors to closely examine the relationships between the fine structure of auditory warnings and the repulsion effects that they produce.

Figure 10. Experiment #2 : Repulsion/indifference/attraction scores (%) for all 45 stimuli. Each stimulus is indicated along the x-axis as a 'source-morphology-IOI duration' triplet, using the abbreviation conventions applied to Figures 789.

ALARM/WILL/SOUND ARTISTIC ADAPTATION

Stemming from the research undertaken over the course of the alarm/will/sound project, Alexander Sigman has been creating works for ensemble, electronics, improvisation and media based upon the alarm prototype sound catalogue, as well as concepts pertaining to and data obtained from the two sound perception experiments associated with this project. Within the framework of the IRCAM Artistic Research Residency, it is intended that the artist-collaborator not only engage in research, but also apply this research in meaningful ways to their creative practice. The examples below illustrate a few such applications of alarm/will/sound concepts, materials and experimental research results to improvised, fixed media, electroacoustic and interactive installation works realised over the past five years.

detritus|reconstructions: Interactive Electronics Improvisation

detritus|reconstructions (2013), a structured laptop improvisation, incorporates, analyses, processes and modulates samples derived from the Paris field recordings falling under the Recorded Auditory Scene sub-category of the sound catalogue (see Figure 1), as well as the Industrial/Mechanical Individual Sound sub-category. These processed samples are presented at varying levels of recognisability-ranging from iconic to thoroughly glitched. Using the insights and abstractness-iconicity gradient obtained from the Experiment #1 results as a reference, frequency, speed and direction shifting, amplitude modulation, and delay/feedback are applied to the selected source-sounds at varying levels, such that sound causation (and context) identifiability is distorted and ultimately minimised. Through the layering of transformed field recording samples, synthetic auditory scenes are constructed, and identifiability of individual sources is further diminished. It is intended that, as the Auditory Scene taxonomy expands, samples will be selected on the basis of performance location.

Future Creatures: Fixed Media Audiovisual Work

A fixed audiovisual media work created using the Python-based concatenative synthesis progam AudioGuide, Future Creatures (2013) was produced in collaboration with Korean animation 10 artist Eunjung Hwang. The sound segments and targets to which segments were mapped in the concatenative synthesis process were derived in part from the Industrial/Mechanical, Vocal Individual Sound (Human and Animal) and Car Alarm Prototype categories of the aforementioned catalogue. In so doing, both the (granulated) surfaces and (spectro-temporal) structure of the source sounds become the core material of the audio. These sound categories were selected in response to the characters, objects and events represented in the animation.

Given the garish nature of the colour palette of the visuals, and lack of 'glitch' elements therein, Industrial/Mechanical and Car Alarm Prototype sounds situated in the acoustic features space depicted in Figure 4 with relatively high PSC and HNR values were chosen. The 11 decisions as to the segment-size of the Industrial/Mechanical source sounds were determined based upon their respective positions along the aforementioned abstractness-iconicity gradient.

That is to say: the more identifiable the source-sound, the smaller the segment-size and the larger the segment density. Echoing the frenetic and densely layered nature of the visuals, these 12 surface fragments and sound morphologies are situated in polyphonic textures, thereby creating http://www.benhackbarth.com/audioGuide/ 10

Given the high level of contrast and relatively small number of entries currently populating the Vocal Individual

11

Sound category, all of these sounds were utilised.

The video may be viewed here: https://vimeo.com/70344676 (accessed 07 April 2018).

synthetic auditory scenes, in which the sources stand in complex and unstable relationships with each other. The auditory events alternately reinforce and conflict with the sense of place conveyed by the visuals.

By dint of the sheer event-density of both the visual and auditory domains, which saturate the senses, changes in the audio tend to trigger attentional shifts in the visual field, and vice versa. It is these cross-modal relationships on the part of the spectator that the composer wished to explore, as opposed to establishing a fixed translation between visual and audio elements. Thus far, no systematic study of the attraction/repulsion levels and internal properties of the audio elements that cause the greatest attentional shifts amongst viewers of the video piece has been conducted. However, as a supplement to Experiment #2, such an investigation is planned for the near future. This type of empirical study would influence both further research into synthetic auditory warning attraction vs. repulsion, and subsequent creative works in which sound sources of a similar nature to those found in Future Creatures are employed.

fcremap and atrocity exhibition: Ensemble + Live Electronics Electroacoustic Compositions

The extraction and application of spectro-temporal features of synthetic alarm source-sounds in the creation of Future Creatures was taken a step further in fcremap (2014-2016), a series of works for instruments, live electronics and visual media, based upon the video and audio of Future Creatures. With respect to raw material in the electronics, entries in the original sound catalogue and the hybrid timbres populating the acoustic feature space described above (and employed in Experiment #2) were incorporated. Throughout the cycle, the timing and direction of physical actions of performers, which trigger routines in the electronics, are influenced by the morphologies and IOI durations of the synthetic alarms used as stimuli in Experiment #2. These morphologies and IOIs were thus treated as abstractions, transposed onto a diverse array of instruments (e.g. various percussion, piano and mixed chamber ensemble), at multiple timescales and degrees of polyphony.

In the cases of the pieces fcremapercremaflvln (for alto flute, violin, electronics and video) and fcremap'dB (for ensemble of quasi-variable instrumentation, animated score and 13 electronics), these abstractions were employed as filters and linear (visual) elements imposed 14 upon the still images comprising the video projection (in the former case) and the animated score (in the latter).

In fclremap (for improvising solo clarinetist and electronics) and fcremap'dB, the performer is presented with a graphic score, consisting of a series of modified images derived

The video projections and performance may be viewed/heard here: https://vimeo.com/167632597 (accessed 07 13 April 2018).

Live performance, with projection of portions of animated score: https://vimeo.com/197745730 (accessed 07 from Future Creatures, and a set of guidelines as to how to interpret the graphics on their respective instruments. The electronics thus function as a kind of 'aural score', which is to be reconciled with visual data. The application of six alarm IOIs to the electronics effectively creates a sequence of six temporal grids, which the performer may resist, use as temporal reference points or (try to) ignore entirely. For fclremap, the clarinetist is also asked to approximate the timbral qualities of the audio segments. The surface and structural features of the alarms, coupled with relatively stable image-to-sound mappings between Future Creatures video elements and the instrumental and electronics material of the fcremap pieces, form a connective tissue for the composer and listener, binding the constituent pieces of the cycle together, despite contrasting performance scenarios and aesthetic characteristics. Scored for horn, electric violin, electric guitar, bass guitar, prepared piano and live electronics, the basic material elements in the electronics and the instrumental parts of atrocity exhibition (2013) stem from Industrial/Mechanical, Animal Call (under the Vocal Individual Sound category) and Car Alarm Prototype entries in the sound corpus. Both the sound morphologies and their respective spectral contents were employed to generate the rhythmic and harmonic structures, as well as playing techniques in the instruments, and the spectral content of the electronics. In the case of the piano, the sources of a few of the industrial/mechanical sounds (ratchet, glass shards, metal chain, metal rod and plastic plectrum) ⎯each of which represents a unique physical material or mode of sound production⎯are utilised on the piano strings. Furthermore, the electronics are projected through a pair of small transducers placed inside the piano (see Figures 13 and14 below). As such, the boundaries between the production and projection of instrumental and electronic sound become blurred. This grey area between mechanical instrument and transducer activation also extends to the violin, electric guitar and bass guitar, whose output is distorted via effects pedals and amplified.

The range of influences and representations of the sound catalogue entries on the musical materials⎯as sources analysed for spectral and temporal content, as processed samples in the electronics and as models of physical causalities in the instruments⎯engenders an abstractioniconicity gradient, along which these materials are situated. This investigation of levels of abstraction, iconicity and sound causality identification reflects the objectives of Experiment #1 (outlined in Section 3.3). In constructing our sound catalogue taxonomy, the perceptual issue of listening modes (acoustic/causal/semantic) arose. Through presenting myriad manifestations across multiple musical dimensions of the original sound sources, and blending acoustic and electronic modes of sonic transmission, it was intended in composing atrocity exhibition to explore and reveal the complexities behind sound source perception and identification, and the preservation, loss or transformation of sonic semantic associations.

Figures 13 and14. Small transducer (front and back) utilised in atrocity exhibition and Minotaur.

Minotaur: Interactive Media Installation

An ongoing audio-visual-kinetic performance-installation collaboration, Minotaur (2012current) brings together music/sound design, video, dance, sculpture, live painting and set design. With respect to alarm/will/sound, it transports the sound categories and features discussed above into the gestural domain, serving as a testing ground for a set of interaction design models that may be incorporated into alarm system prototypes emerging from our research and development activities. Based upon twentieth century re-castings of the myth of 15 the Minotaur, the locus of activity is a labyrinth constructed out of Plexiglas panels. Inside the 16 labyrinth, dancers' movements are detected via microphones and sensors, which are processed in the electronics. In turn, audio output is transmitted to an array of transducers (similar to the model used in atrocity exhibition), affixed to the Plexiglas as well as to hollow sheet-metal sculptures at the centre of the labyritnth (see Figure 15). Consequently, the sculptures, which symbolise the primal desires and tendencies of the Minotaur, function as resonators, and the audio produces Plexiglas vibrations, which influence the dancers' actions. A feedback loop between sound generated inside the labyrinth and audio produced external to it is thus set in motion. Adaptable for gallery, concert and even telematic performance contexts, sounds 17 recorded directly outside of the venue during the performance/exhibition may be integrated into the audio, thereby localising the constructed labyrinth environment.

Audio events may be self-triggered at random intervals, manually activated by the composer or triggered in response to activity within the labyrinth. In all cases, the temporal patterning of the synthetic alarm sounds used in Experiment #2 has governed the number and rate of iterations of triggered sonic elements, with sounds intended to repel dancers from their current respective locations assigned medium and short-duration IOIs. However, changes in 18 physical parameters within the labyrinth cause shifts in the spectral and spatial properties of the triggered sound materials over time. Such a gesture-sound relationship introduces a degree of unpredictability to the electronics.

Given the feedback loops between labyrinth-internal and labyrinth-external audio, this unpredictability is compounded, and impacts not only the auditory, but also the tactile and kinetic dimensions of the constructed labyrinth environment. Consequently, the interaction model For a discussion of these proposed gesture-sound interaction design models, see Misdariis and Sigman 2016: Here is an example of a networked rendition of Minotaur, involving performers in New York and Oakland, CA: In the myth, as both a dangerous predator and the target of assassination for Theseus and his less-fortunate becomes multi-modal, multi-directional and time-varying. In the gallery context, audience members have the opportunity to enter the labyrinth and engage with the sensor/actuator system. To an extent, the installation version of Minotaur serves as a model for an eventual interactive public presentation of our alarm systems under development, as described below.

CONCLUSION AND FUTURE RESEARCH DIRECTIONS

This paper was intended to outline how the alarm/will/sound project, predicated on the obsolescence of the audible car alarm, and informed by principles of the acousmatic tradition, has developed in multiple (and at times, unexpected) sound design, perception and modelling research and artistic directions. Although the creative works that the auditory warning design and research aspects of the project have influenced may bear few aesthetic parallels to those associated with musique concrète, the ecological sound classification models and listening mode categories emerging from research closely connected with the Schaeffer legacy, as well as the results of the sound perception experiments conducted under the auspices of alarm/will/sound, have borne a significant impact. As such, it is hoped this open-ended and multi-faceted project serves as an innovative example of productive cross-pollination between research activities and artistic practices.

The various dimensions of alarm/will/sound bear a broad range of possible future artistic and research applications. Besides further studies in car alarm systems specifically, or auditory warnings/audio signals in public spaces, our approach to sound classification, research into sound abstraction/iconicity and creation of a customised (yet adaptable) acoustic descriptor space could inform burgeoning musical information retrieval areas such as searchable sound database construction and tagging. Along similar lines, the measurement of auditory warning attraction and repulsion levels (in Experiment #2) and re-purposing of the car alarm as a localised audio communication device would intersect with the interests and objectives of noise pollution and ecological sound data collection, analysis and visualisation projects (such as NoiseTube, Citygram or SONYC). 19 With respect to artistic extensions of the project, it would be of particular interest to explore auditory warning abstractness vs. iconicity, geolocalisation and attraction/repulsion levels in greater detail. The former two could be addressed in the context of a crowdsourced interactive media installation, for instance. In this scenario, real-world and synthetic auditory warning recordings would first be collected from contributors from across the world and stored in a database. These source-sounds could then be presented in different combinations in objects distributed across a physical and/or virtual installation space. Listeners would be asked in this context to rate the levels of recognisability and attraction/repulsion. As an ecologically-based sound perception experiment, this would enable the authors to examine the relationship between familiarity and attraction/repulsion dimensions of globally-sourced stimuli. The sounds could then be tagged accordingly, and retrieved from the database via the appropriate search terms.

Figure 1 .

 1 Figure 1. Alarm sound catalogue taxonomy, constructed in January-February 2013. 'Elec' and 'Mech' (under INDUSTRIAL/MECHANICAL=>Appliance) refer to a distinction between electrically-powered and mechanical sources, respectively. 'Concat' (under SYNTHETIC/ ELECTROACOUSTIC =>Grains) refers to the concatenative synthesis technique by which the entries in this sub-category were generated.

Figure 2 .

 2 Figure 2. Experiment #1 user interface.

Figure 3 .

 3 Figure 3. Experiment #1 confidence ratings for 39 stimuli. Mean scores, as well as maximum and minimum standard deviations are indicated. The thick black line indicates the iconicity threshold.

Figure 4 .

 4 Figure 4. Acoustic space built upon two perceptually relevant acoustic features taken from (Misdariis et al. 2010): Perceptual Spectral Centroid (PSC) and Harmonic/Noise Energy Ratio

Figure 5 .

 5 Figure 5. Taxonomy of temporal morphologies of environmental sounds (from [Minard et al. 2010]).

Figure 6 .

 6 Figure 6. Six car alarm signals (#1 to #6 from top to bottom). For each, the grey segment of the curve is the elementary component used for computing the IOI of the whole auditory warning signal.

Figures 7- 9 .

 9 Figures 7-9. Experiment 2 (L-R): repulsion/indifference/attraction scores (%) with respect to the five 'source' categories; repulsion/indifference/attraction scores with respect to three 'morphology' categories (decreasing ['dec'], increasing ['inc'] and single impulse ['sin']); repulsion/indifference/attraction scores with respect to IOI durations (long ['lon'], medium ['med'] and short ['sho']).

Figure 11 .

 11 Figure 11. An excerpt of the first movement of the fcremaperc score, scored for tam-tam, resonating metal sheet and live electronics. The pitch contours and IOIs of synthetic alarms utilised in Experiment #2 are transposed onto the right-hand and left-hand actions of the performer.

Figure 12 .

 12 Figure 12. An excerpt of the fcremap'dB score, with synthetic alarm temporal patterns and pitch contours inscribed.

  House of Asterion', Swiss author Friedrich Dürrenmatt's Minotaurus and Russian novelist Victor 16 Pelevin's Helmet of Horror in particular.

  17 https://vimeo.com/46066079 (accessed 15 August 2018).

Figure 15 .

 15 Figure 15. Sheet-metal sculptures (with transducers attached), dancer, and Plexiglas projections, in one version of Minotaur.

Table 1 .

 1 Table 1 below for exact values.) http://sites.stat.psu.edu/~mga/401/tables/Chi-square-table.pdf Results of the Chi 2 statistical test: probability of the Chi 2 (p), Chi 2 value (χ 2 ), degree of freedom (DoF) for the whole dataset (ALL) and the factorised data (Source, Morphology and IOI duration). * = statistically significant: H0 rejected.

	P	Χ 2	DoF

and https://freesound.org/, respectively.

intuitiveness in the use of an object (Norman 1999).

features. That is to say: when designing abstract melodic auditory warnings, how to obtain the best results for learnability and differentiability amongst stimuli[START_REF] Schalkwijk | Alarm and Auditory-Interface Design: Learnability of alarms and auditory-feedback for random and meaningful alarm sounds investigated in a paired-associate paradigm[END_REF].

https://www.mathworks.com

(accessed 14 September 2018).

April 2018).

predacessors, the Minotaur both repels and attracts (at least in a goal-direction sense). As such, it is appropriate that sounds with both high and low repulsion levels are emitted within the labyrinth.


Nicolas Misdariis
email: nicolas.misdariis@ircam.fr

Camille Dianoux
email: camille.dianoux@ircam.fr

Lucie Marignier
email: lucie.marignier@gmail.com

Raphaël Leiba
email: raphael.leiba@sorbonne-universite.fr

Community Noise Quality Assessment (Annoyance) by means of a Virtual Audio Environment

Keywords: Noise, Environment, Annoyance I-INCE Classification of Subject Number: 63 ; 79 1

This study deals with community noise quality assessment (annoyance) of urban environments. It is based on a previous work that resulted in: a) a road-traffic vehicle audio signals extraction tool, allowing to estimate different sonic properties; b) a modeling tool that estimates the perceived annoyance level by adapting a current multiclass psychoacoustic model, given in the literature. The present work follows these outputs and aims at validating experimentally the annoyance estimations. A listening test is designed in order to be able to collect perceived annoyance in a virtual environment. Sound scenes made of urban soundscapes (background) and vehicles pass-by (foreground) are built and encoded in a 2D to multichannel algorithm, in order to create a listening experience as ecological as possible. Then, a consistent experimental protocol is designed in order to measure the perceived annoyance caused by each synthesized sound scene. The focus is put on the spatial audio environment that is used for the perceptual experiment. Especially, urban sound scene synthesis methods and tools are presented. Results from the perceived naturalness and immersion collected during the test are presented and analyzed. The global issue addressed by the use of virtual reality in laboratory experiments is finally discussed.

INTRODUCTION

The present work is undertaken in the large frame of acoustic ecology, and more precisely concerns urban soundscapes and community noise assessments. Within a global research program that dealt with life quality in urban soundscape (Mouvie, 2014-17), we initially postulate that -either for both air quality or noise exposure -, one of the main sources of urban pollution is related to mobility, that is to say largely consists in road vehicles that daily populate more or less all the cities worldwide.

Since the seminal works of R.M. Schafer and colleagues within the World Soundscape Project [START_REF] Schafer | The tuning of the world[END_REF], a lot of scientific studies and applied works have been done to investigate, qualify, understand, and sometimes improve urban sonic environments, and indirectly their inhabitants' daily life. In that scope, since the last decades and among much others, we can cite the 2002/49/CE European directive that aimed at establishing strategic soundmaps -expressed in sound level L den -, that force the politics to address these issues. As an alternative to this 'normative' answer to noise pollution, a more 'sensitive' way of considering the problem [START_REF] Chetelat | La figuration cartographique de l'espace sonore. Images Re-vues[END_REF] came some years ago from the European COST Action "Soundscape of European Cities and Landscapes" (TD0804, 2013-17)) which claimed to consider environmental sounds as "a 'resource' rather than a 'waste'" [START_REF] Schulte-Fortkamp | Soundscape-focusing on resources[END_REF] arguing that "reducing sound level does not necessarily lead to improved quality of life in urban / rural areas" (see [START_REF] Misdariis | Mobility and life quality relationships-Measurement and perception of noise in urban context[END_REF], for more details). More recently, a collaborative research project, EUREQUA , aimed at taking into account these two different approaches by objectifying and assessing the environmental quality at neighborhood scale through the identification of relevant criteria related to: i) the characterization of the physical environment (acoustics, but also air quality or climate); ii) the evaluation of the quality of life in the neighborhood by residents and users [5]. By means of an interdisciplinary and participatory dimensions, this kind of projects tends to make the (missing) link between what is measured and what is perceived in terms of community noise exposure.

In the same point of view, a PhD work has been recently defended (2017), that dealt with the development of a tool able to diagnose the perceived noise annoyance by inhabitants in a urban context [START_REF] Leiba | Conception d'un outil de diagnostic de la gêne sonore en milieu urbain[END_REF]. This work globally relies on 3 main phases: i) the measurement of urban sound scenes, to determine, isolate and characterize single sound sources -in that case -of passing-by road vehicles; ii) the classification of sound events forming these scenes into perceptually coherent categories and represent them in a more synthetic manner; iii) the modeling of the noise annoyance generated by these sound scenes, to estimate the perceptual sensations felt by their neighborhood.

The first phase mainly relies on a multi-channel acquisition device (Megamicros) used with a beamforming processing. It allows to parse and extract isolated sound sources from a complex sound scene. Megamicros fits in acoustic imaging systems but is developed upon an original digital MEMS (Micro Electronic and Mechanical System) microphones array that can manage devices from a few tens to more than a thousand of sensors, scattered over a large area (typically several meters). Moreover, a video tracking algorithm allows to localize moving sources over an extended sound scene in order to extract their contributions to the overall emission with beamforming in frequency domain. In the time of the PhD work, this technical apparatus has been validated with outdoor implementations both at the vehicle scale (controlled single sources) and the street scale (flow of uncontrolled multiple sources). In particular, the validation at the vehicle scale was operated during a measurement campaign in a professional automotive testing environment (La Ferté-Vidame, exploited by PSA Peugeot-Citroën) and produced a database of controlled passing-by for different vehicles representative to the urban traffic (see [START_REF] Leiba | Urban acoustic imaging: from measurement to the soundscape perception evaluation[END_REF] for more details on this phase).

The second phase consists in classifying the extracted audio signal over perceptual categories thanks to machine learning. This work is done upon the results of Morel et al.'s perceptual study of vehicle sound sources [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF]. They outlined a perceptual representation of the soundscape related to mobility (vehicles) explained by two factors: vehicle types (light, heavy, 2-wheels) and driving conditions (acceleration, deceleration, constant speed). The work led to 7 categories: Cat.1 (2-wheels, constant), Cat.2 (2wheels, accel.), Cat.3 (light+heavy, constant), Cat.4 (2-wheels, decel.), Cat.5 (light+heavy, decel.), Cat.6 (light, accel.), Cat.7 (heavy, accel.). From that, a Support-Vector Machine (SVM) using MFCC (Mel-Frequency Cepstrum Coeff.) description of the source signals -but also the driving conditions (extracted from trajectory tracking from the video analysis) -allows to build an automatic classifier able to assign an input recorded data in one of the seven Morel et al.'s categories, with an overall estimation error around 15% (see [START_REF] Leiba | Large array of microphones for the automatic recognition of acoustic sources in urban environment[END_REF] for more details on this phase).

The third phase is the core of the present paper. It is done upon the results of Morel and colleagues perceptual modeling of vehicle sound sources [START_REF] Morel | Noise annoyance assessment of various urban road vehicle pass-by noises in isolation and combined with industrial noise: A laboratory study[END_REF]. In fact, for each of the perceptual category of vehicles (see above), they defined a model based on acoustic and psychoacoustic features, computed on the input signal, and able to deliver the estimation of a specific annoyance related to the given category. During his PhD, Leiba implemented and refined this model, especially with regards to computation of some psychoacoustic features (roughness, and fluctuation strength). The last step of this third phase then consists in closing the loop of the process with a quantified evaluation of the model, i.e. a compared analysis of computed vs. measured annoyance data.

We implement a laboratory protocol based on a virtual audio environment, inspired by literature and a previous work done for sound design purpose [START_REF] Misdariis | Urban environment audio simulation for contextural evaluation of qient vehicles' sound design[END_REF]. This environment is used to render audio realism, in order to generate valuable perceptual judgment on semi-virtual sound scenes, additionally controlled in terms of composition (type of category) and sound level.

In this paper, we will, first, briefly present the perceptual noise annoyance model that has been implemented (Sec. 2). This model will be challenged in Sec. [START_REF] Schulte-Fortkamp | Soundscape-focusing on resources[END_REF]. In details, the experimental approach will be developed (Sec. 3.1) and finally some comparisons between modeled and measured annoyance data will be presented (Sec. 3.2). A special focus will be given on the constitution of the virtual audio environment by addressing these three main questions : From what and how the stimuli have been designed ? With which hardware / software architecture the sound scene realism has been rendered in laboratory conditions ? How the realism has been felt and assessed by the participants ?

PERCEPTUAL NOISE ANNOYANCE MODEL

The present perceptual noise annoyance model belongs to a category that tries to explain annoyance by multi-linear combinations of acoustic and psychoacoustic features computed from the signal -in that case, a vehicle noise. This approach does definitely not include a non negligible part of perceived annoyance that involves other factors than physical or physiological -for instance social, demographic or cultural (see Berglund et al. for a general development on that point [START_REF] Berglund | Total annoyance models for community noises explicated[END_REF] or de Coensel et al. for an example of such a multi-factorial model [13l]). Nevertheless, the present work assumes a rather low level initial approach that was scientifically built and validated few years ago.

Foundations

The model starts from Morel et al. works that established relationships between several acoustic / psychoacoustic features and specific annoyance for each perceptual vehicle categories [START_REF] Morel | Noise annoyance assessment of various urban road vehicle pass-by noises in isolation and combined with industrial noise: A laboratory study[END_REF] -that they also stated in a previous work [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF] (see Introduction).

Resulting from this work, and in total, the features used to model annoyance in one -or several -categories are the followings: -N : overall loudness (time averaged), -N 15-18 : specific loudness for Barks 15 to 18 (time averaged), -R max : roughness (maximum value) -F, F max : fluctuation strength (average and maximum value) -!N -: loudness decreasing rate (after the passing of the vehicle) -L MF : A-weighted level in the medium frequency range (315 -1250 Hz, 1/3 octave)

The precise analytical expressions (extracted from Morel et al.'s outcomes [START_REF] Morel | Noise annoyance assessment of various urban road vehicle pass-by noises in isolation and combined with industrial noise: A laboratory study[END_REF]) describe the specific annoyances (A n ), in the form of linear regression models that have been, in addition, perceptually validated (R 2 scores between 0.91 and 0.97, p <0.05):

A 1 = 1.03.N + 0.18 A 2 = 16.99.N 15-18 + 0.10.F + 1.45 A 3 = 1.32.N -0.32.!N --0.36 A 4 = 0.89.N + 0.02.R max + 0.33 A 5 = 1.07.N + 0.08.F max -1 A 6 = 0.29.L MF -8.5 A 7 = 0.95.N + 0.10.F -0.5

Implementation

In more details, and regarding the most sensible features, it appears that: i) loudness N is used in all categories except Cat.6 (light vehicles in acceleration), and the loudness decreasing rate !N -is used in Cat.3 (light/heavy vehicles at constant speed); ii) roughness R is used in Cat.4 (2-wheel vehicle in deceleration); iii) fluctuation strength F is used in Cat.2-5-7 (respectively, 2-wheel in acceleration, light/heavy vehicle in deceleration and heavy vehicle in acceleration).

For our purpose, loudness is computed with Fastl and Zwicker's instationnary loudness model [START_REF] Fastl | Psychoacoustics: facts and models 22)[END_REF], implemented by Genesis (Loudness Toolbox). Roughness is adapted from Daniel and Weber's model [START_REF] Daniel | Psychoacoustical roughness: Implementation of an optimized model[END_REF], inspired by Garcia's implementation [START_REF] García | Modelling the sensation of fluctuation strength[END_REF], and improved in order to better fit the theoretical curves established by Fastl and Zwicker [START_REF] Fastl | Psychoacoustics: facts and models 22)[END_REF]. The optimization is done in a parametric way, by adjusting 5 structural parameters of the roughness model and minimizing a cost function that represents the distance between the digitalized theoretical curves and the computed data from the combinatory model. A similar protocol is used to adapt and optimize the fluctuation strength model, implemented by Osses Vechhi et al. [START_REF] Osses | Modelling the sensation of fluctuation strength[END_REF], on the basis of the theoretical expression established by Fastl and Zwicker [START_REF] Fastl | Psychoacoustics: facts and models 22)[END_REF].

These modified seven models have finally been implemented and tested with real data (sound signals) acquired during two different measurement campaign on either a predefined traffic with controlled outdoor conditions (La Ferté-Vidame) and an undefined traffic with uncontrolled outdoor conditions (St-Bernard Quay, Paris). In brief, the results are congruent with a more classical measurement (L A,eq ) but offer an interesting reduction of data variability (see [START_REF] Leiba | Conception d'un outil de diagnostic de la gêne sonore en milieu urbain[END_REF] for more details).

EXPERIMENTAL EVALUATION OF THE PERCEPTUAL MODEL

The experimental validation of the noise annoyance perceptual model presented in Sec. 2 forms the core of the present work. It aims at validating the whole research process by comparing numerically computed data (from the model) with perceptually measured data (from the experiment). In this section, we will especially pay attention to the environment implemented for conducting the experiment in an ecological way, i.e. the way to place the participants in realistic conditions for noise annoyance assessments. This being, the Results sub-section (Sec3.2), will first focus on the assessment of the context (realism, immersion) before delivering main results on the percept (noise annoyance) -that is more developed in a complementary paper [START_REF] Misdariis | Experimental validation of a urban traffic noise annoyance model[END_REF].

Evaluation environment

In the light of experimental realism and immersion, the evaluation environment gets mainly three components answering three main questions: what do participants hear (stimuli) ? How are they acoustically contextualized (sound rendering) ? How are they mentally contextualized (experimental procedure) ?

Stimuli

On the basis of the generic description of a sound sequence by Nelken and de Cheveigne (2013) [START_REF] Nelken | An ear for statistics[END_REF]: "a skeleton of events on a bed of texture", the stimuli of this experiment result from the sound design of sequences based on two main components, edited and mixed in a DAW -Digital Audio Workstation (Logic Pro):

• a background noise -the "bed of texture" -coming from a database of field recording sounds in outdoor spaces (Paris streets and avenues) initially realized with two different devices: an ORTF couple (Schoeps MSTC 64U) and an order-1 Ambisonic microphone (Soundfield ST250). This campaign was part of a previous work that aimed at developing a urban environment audio simulation for contextual evaluation of quiet vehicles' sound design [START_REF] Misdariis | Urban environment audio simulation for contextural evaluation of qient vehicles' sound design[END_REF]. Those recordings were realized by two professional sound engineers and were made at different places in order to have a sample of sound atmospheres from calm zones (narrow lanes, quiet zones, etc.) to very noisy areas (large avenues, with a high traffic flow, etc.).

• a set of specific vehicle sounds -the "skeleton of events" -coming from a database of sound recordings (and processings) obtained within the frame of Leiba's PhD [START_REF] Leiba | Conception d'un outil de diagnostic de la gêne sonore en milieu urbain[END_REF] with the help of automotive infrastructures provided by a french car manufacturer (PSA Peugeot Citroen). This campaign consisted in recording separately a large set of different vehicles while controlling either types (light/heavy/2-wheel vehicles), engine specifications (number of cylinders and/or engine size), dynamics (acceleration, deceleration, constant speed) and recording context (a quiet, isolated place without noise pollution). The sound recordings were realized with three different devices: a reference monophonic microphone (B&K 4190), a binaural device (HEAD acoustics Artificial Head) and a 2D-microphonic array (256-channel MEMS microphones). The initial aim of this set up was to record calibrated samples for each perceptual vehicle category, as defined by Morel et al. [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF].

On this basis, for the design step of the sound sequences -and after several rounds of expert listening -, we finally chose the stereo ORTF recordings for the soundscape source and the monophonic recordings for vehicle sources, as being the best compromise between sound quality and immersive capacities. The soundscape was edited in order to create a neutral -or amorphous as defined by [START_REF] Maffiolo | De la caractérisation sémantique et acoustique de la qualité sonore de l'environnement urbain[END_REF] [START_REF] Maffiolo | De la caractérisation sémantique et acoustique de la qualité sonore de l'environnement urbain[END_REF], and used by [START_REF] Dubois | A cognitive approach to urban soundscapes: Using verbal data to access everyday life auditory categories[END_REF] [START_REF] Dubois | A cognitive approach to urban soundscapes: Using verbal data to access everyday life auditory categories[END_REF] -sequence, without any noticeable event to be heard. Vehicle sounds are then selected for each of the 7 perceptual categories and if needed edited to make a given category clearly recognizable -for instance, by manually reshaping the increasing or decreasing level profile of respectively acceleration or deceleration -and also to give them spatial properties -basically, left/right trajectories.

The output of this first technical step resulted in: i) a background sequence able to be looped without detecting any audio artifact; ii) 7 sets of vehicle samples (among Morel et al.'s categories) able to be randomly played at any time on the foreground of the sequence. This output will be used hereafter for building, in realtime during the experiment, the mixed urban sequence to be assessed according to annoyance.

Sound rendering apparatus

On the basis of seminal works done -among others -by Guastavino and Katz (2004) according to perceptual evaluation of multi-channel spatial audio reproduction architectures [START_REF] Guastavino | Perceptual evaluation of multi-dimensional spatial audio reproduction[END_REF], we choose to use a 5.1 broadcast system is used as a compromise between ecological and immersive aspects, especially with regards to the binaural technology. Indeed, despite its great immersive quality, the binaural implementation may prevent from moving freely without being embarrassed by headphones and may also require a sensitive calibration procedure.

The set-up is installed in a mastering professional studio (Ircam-Studio 8surface area = 47 m 2 , TR = 200 ms.) by means of a 5.1 PSI monitoring system (model A25-M + A225-M subwoofer) conventionally distributed in the room (0°, +30°, -30°, +110°, -110°), within a 2,4-meter diameter circle (Figure 1). Moreover, note that no specific equalization of the system is done for the current experiment. The digital software used with this hardware set-up is the Spatialisateur (Spat © ), a virtual acoustic sound rendering engine internally developed since few decades [START_REF] Jot | A real-time spatial sound processor for music and virtual reality applications[END_REF], and especially its most recent released module, Panoramix [24]. In our case study, this processing environment allows to encode the two groups of stimuli (background and events) in the 5.1 format, targeting both a diffused spatialization for the background and controlled localization trajectories for the events (see Figure 2 for a block diagram of the implemented audio process) The background component is set by adjusting the aperture of the stereo image, encoded in the 5.1 format by means of a VBAP algorithm [25] -perceptually adjusted by the co-authors expert team in order to maximize the immersive impression. With the same perceptual/empirical approach, the event components are virtually placed in front of the audio set-up at a realistic distance with regards to an outdoor daily experience.

Experimental procedure

The experimental procedure is mainly inspired by two previous studies dealing with noise annoyance measurement of industrial and environmental sounds.

First, [START_REF] Morel | Noise annoyance assessment of various urban road vehicle pass-by noises in isolation and combined with industrial noise: A laboratory study[END_REF] conducted a laboratory study where participants were asked to assess their perceived annoyance while listening to pass-by sound sequences (representing perceptual categories that the same team determined previously [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF]). The context and the task were simply presented to the participants by the following sentence: "Imagine yourself at home while relaxing (e.g. reading, watching television, discussing, gardening or doing other relaxing activities you are used to). While you are relaxing, you hear this road traffic noise. How much would this noise annoy you?''. Answers could be given in a continuous numerical scale with regularly spaced semantic labels from "not at all" to "extremely" [START_REF] Morel | Noise annoyance assessment of various urban road vehicle pass-by noises in isolation and combined with industrial noise: A laboratory study[END_REF].

Second, [START_REF] Brocolini | Effect of masking noise on cognitive performance and annoyance in open plan offices[END_REF] conducted a laboratory listening test to assess the sound annoyance in open-plan offices. The sequences were composed of 5 different sound conditions -4 different sources and 1 control condition (printer sounds, intelligible / unintelligible human voices, phone rings, air-conditioner background noise). For each sequence, a task consisting in recalling words in a list put the participants in a cognitive context similar to a reading activity and provided a performance measure. After each sound condition (several sound sequences of the same type), a NASA-RTLX questionnaire is made to estimate participants' mental workload. Then, at the very end of the experiment, an annoyance measurement was realized by answering the following question : "At which point did the sound sources affect you in accomplishing the task requested?", by means of a continuous scale for each sound sources -that could also be recalled by playing back a 5-second excerpt of it [24].

On that bibliographic basis, we have built an experiment that reuses some of these components and introduces new ones. In fact, the experimental design considered the 7 vehicles categories already defined (Sec. 3.1.1) -plus a control condition where the background noise is played alone -presented at 4 different sound levels (from 50 to 62 dB(A) by 4-dB step), so that the sound corpus is made of 32 sound sequences. Each sequence contains 3 events of a given category randomly distributed in a 25-second sample of background noise set at 45 dB(A) -or the background noise alone, in the control condition (repeated 4 times, at the same level of 45 dB(A)). The presentation of this combination (categories vs. sound levels) is randomized for each participant.

Then, we divide the experiment in two distinct protocols with regards to dependent (measured) variables : a direct one and an indirect one. The direct protocol only consists in asking, after each of the 32 sequences, the perceived annoyance during the listening, assessed in a continuous numerical scale (0 to 10). The indirect protocol involves a cognitive task that consists in recalling words from a list visually showed (on a computer screen) during the playback of the sound sequence. The lists are made of words taken from Dubois and Poitou's lexical lists [25]. Sixteen lists of twenty commonly cited words in general domains (like animals, trees, fruits, sports, professions, etc.) are selected. The recall performance is done by a verbal return. This protocol also includes a participant's workload estimation by means of a NASA-RTLX questionnaire [START_REF] Hart | NASA-task load index (NASA-TLX); 20 years later[END_REF] that is asked at the end of each sound sequence together with the annoyance estimation (made in the same way than for the direct protocol). To sum up, the three dependent variables considered are: the (subjective) perceived annoyance, the (subjective) workload and the (objective) recall performance.

Moreover, general question are also asked at the very end of the experiment (after the 32 sequences). They mainly concern the fatigue felt after the test and, above all, the assessment of the experimental realism by means of a mark in a continuous 0-10 scale (0 = not realistic at all; 10 = very realistic), together with free comments.

A total of 36 participants (18 for direct, 18 for indirect protocol) took part to this experiment. They were aged from 19 to 48 years old (mean age = 26) quite equally distributed in terms of gender (19 women, 17 men). Moreover, they all declared not having neither serious hearing disorders (tinnitus, implants, etc.) nor neurological antecedents (epilepsy, etc.). For each of them, the experiment lasted approximately 1 hour including a break (indirect protocol), or 45 minutes for the direct protocol in which the 32 sound sequences are played twice in two consistent blocks separated by a break.

Results

Data collected are still in progress in terms of analysis. For this paper, and in this section, we focus first on experimental audio environment assessments (Sec. 3.2.1), and give some main results -further developed in [START_REF] Misdariis | Experimental validation of a urban traffic noise annoyance model[END_REF] -about the global purpose of this work, i.e. concerning the perceptual annoyance model (Sec. 3.2.2).

Realism of the experimental audio environment

The realism of the audio set-up can be estimated with the judgment score and additional comments given by each participant at the very end of their test. They had to answer questions orally asked by the experimenter about the "realism of the sound environment", and if needed were asked to give additional comments about the "feeling of immersion during the test" and the "benefits and drawbacks of the experimental apparatus". The judgment on realism is done on a continuous numeric scale from 0 (not realistic) to 10 (very realistic) and the comments result from a free verbalization.

The numerical data (36 scores between 0 to 10, divided in two groups : direct or indirect protocol -see Sec. 3.1.3) show a good assessment of the apparatus (average = 8.4) and a low variability of these judgments (standard deviation = 0.9). Moreover, this global result tends to be consistent within the two groups of participants, according to the nature of the protocol (direct vs. indirect) -see Table 1 In addition, it is worth noticing that most of the free comments were rather positive about the realism and show a number of occurrences of the terms 'realistic', 'very realistic', 'realism ok' or similar around 20 (above 36) with sentences like: "very impressed by the sound rendering' (Subj. #31), "very realistic, especially the background noise" (Subj. #30), or "very realistic indeed, I thought that some sounds came from the outside of the studio" (Subj. #6).

Nevertheless some criticisms were formulated, especially with regards to the unrealistic low level of human voices ("people usually talk louder in the real life", Subj. #24), the level of some events from the heavy vehicle categories (played at the loudest value of 62 dB(A)) that seemed too loud for some participants, and a relative consensus on the fact that the deceleration scenario was felt to be the less realistic of whole set of sound scenes: "decelerations are not very realistic, the vehicles stop too frequently and too quickly" (Subj. #35), "deceleration not realistic, they stop suddenly" (Subj. #36), "too much vehicles that stop in one go, in the same time" (Subj. #33).

Global results on perceptual annoyance

This section looks into the perceptual data collected according to the experimental perceptual noise annoyance assessed in direct and indirect protocols. First of all, Figure 3 shows that, at a 1 st order of analysis, these two sets of data can be considered similar in average. Indeed, the variance of these two sets of data is explained by a significant Pearson's correlation coefficient of 0.90 and their distribution fits well with the corresponding linear regression model. Secondly, a look into relationships between the main measured variable (annoyance) and one of the experimental factor (sound level), in Figure 4, leads to a common result stating that annoyance increase positively with noise exposure level -on which the current soundmap approaches are incidentally based. Currently, this result seems confirmed for all perceptual vehicle categories (nearly parallel curves) but also shows a rather high variability among values (offsets and large span of annoyance values, actually, between Cat.3 (light+heavy vehic. at constant speed) and Cat.7 (heavy vehic. in acceleration). In a way, this latter observation legitimates the perceptual approach which tends to refine this 1 st level of modeling. Thirdly, the fitting between estimated (modeled) and measured (perceived) data of annoyance is investigated and depicted in Figure 5. It allows us to get an interesting result that confirms Morel et al.'s outcomes whereas it has been proposed in another scientific context and different experimental conditions. In fact, this result shows that the perceived annoyance of different vehicle categories and the corresponding modeled annoyance are well correlated and that the model explains a significative part of the variance between these two sets of data (R=0.89, R 2 =0.79, p<.001). Figure 5 shows mainly three things: i) the points are well distributed along the regression line so that validates the linear relation between measurement and modeling; ii) this being, some categories fit better than others; for instance Cat.2 tends to have a slightly different slope (2-wheel vehic. in acceleration -modeled by narrow band loudness, N 15-18 , and fluctuation strength, F) or Cat.6 tends to be rather non linear (light vehic. in acceleration -modeled by equivalent sound level in medium frequencies, L MF ); iii) the estimated annoyance seems to be always twice as much the measured value. These issues are deeper investigated in [START_REF] Misdariis | Experimental validation of a urban traffic noise annoyance model[END_REF].

CONCLUSIONS

This paper presents the whole synopsis of a large scale research project that investigates the characterization of urban soundscapes, with regards to road traffic sources [START_REF] Leiba | Conception d'un outil de diagnostic de la gêne sonore en milieu urbain[END_REF]. It focuses mainly on the last part of this project that deals with experimental validation of a perceptual noise annoyance model, based on previous works [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF][START_REF] Morel | Noise annoyance assessment of various urban road vehicle pass-by noises in isolation and combined with industrial noise: A laboratory study[END_REF].

We detail the implementation of a laboratory set-up that relies on a virtual audio environment able to render as ecologically as possible urban sound scenes with different vehicle sources -considered as responsible for the perceived noise annoyance -in the foreground,. This percept is measured with an experimental protocol that tends to compare different cognitive listening situations (direct or indirect paradigm).

The results obtained show a global positive assessment of the realism of the audio scenarios, together with a rather good feeling of immersion inside the sound scenes. Moreover, the annoyance theoretical model seems to significantly correlate with the data experimentally obtained, and to be a more relevant answer to the noise annoyance estimation than the classical equivalent sound level (L A,eq or L den ).

On that basis, the perspectives of this work is two-fold i) to improve and consolidate the results already obtained by refining the analysis -this part is mainly done in a current complementary development [START_REF] Misdariis | Experimental validation of a urban traffic noise annoyance model[END_REF] ; ii) to improve the model itself, either with regards to some psychoacoustic features that build the model, or in terms of global annoyance, leading to an important -an yet unresolved -issue that focuses on the summation process occurring in front of a complex mixture of sound events.

Figure 1 .

 1 Figure 1. the 5.1 set-up installed in the mastering studio 8 at Ircam (© LCA/Culturebox)

Figure 2 .

 2 Figure 2. Block diagram of the audio architecture implemented for the experiment: from the sources selection composing the sound sequence (left), to the 5.1 diffusion system installed in the studio (right)

Figure 3 .

 3 Figure 3. Linear regression between averaged data of annoyance assessed in the direct protocol (mean of block 1 and 2) and indirect protocol. Each point corresponds to a vehicle category (1 to 7) and a given sound level (50 to 62 dB). Pearson's coefficient gives a significant correlation of 0,90, (p <.001)

Figure 4 .

 4 Figure 4. Influence of events noise level on perceived annoyance evaluation for each step of this factor (50 to 62 dB(A) by 4-dB step). Each line corresponds to a vehicle category (see Introduction).

Figure 5 .

 5 Figure 5. Linear regression between estimated annoyance and experimentally measured annoyance (direct protocol). Each point corresponds to combination of vehicle category (1 to 7) emitted at a specific noise level (50 to 62 dB(A)). Pearson's coefficient gives a significant correlation of 0.89, (p < 0,001)

Table 1 .

 1 for details. Analysis of numerical data on realism judgment (0: not realistic -10: very realistic) collected from the 36 participants having passed either the 'direct' or 'indirect' experimental protocol (18 persons in each)

		Mean	Stand. Deviation
	All	8.3889	0.9420
	direct group	8.4722	0.9467
	indirect group	8.3056	0.9570

ACKNOWLEDGEMENTS

The authors are thankful to Prof. Bert De Coensel and Prof. Massimiliano Masullo for their invitation to participate in the Technical Session on " Virtual Reality for Noise Control and Sound Quality " of the Internoise 2019 conference. This is in line with the research made within the Chair "Mobilty and Quality of Life in Urban Surroundings" (2014-2017) led by The UPMC Foundation and sponsored by the Donors (PSA Peugeot-Citroën and Renault).