Nicolas Misdariis
email: nicolas.misdariis@ircam.fr

Lucie Marignier
email: lucie.marignier@gmail.com

Camille Dianoux
email: camille.dianoux@ircam.fr

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

EXPERIMENTAL VALIDATION OF A URBAN TRAFFIC NOISE ANNOYANCE MODEL

Keywords: urban soundscape, annoyance, measurement, perceptual model, assessment

This study deals with noise annoyance in urban environments. It is based on a previous work that resulted in two main outputs. First, a measurement tool that identifies and extracts sonic properties of the main urban noises sources, i.e. the different types of vehicles. Second, a modeling tool that estimates the perceived annoyance level by adapting from the literature, and implementing, a current multi-class psychoacoustic model. The present work follows these outcomes and aims at validating the annoyance estimations given by the model with an experimental approach. A listening test is designed in order to be able to collect perceived annoyance in a virtual environment (in lab test). Sound scenes made of urban soundscapes (background) and vehicles passing-by (foreground) are built and encoded in a 2D-audio immersive format (2D to multichannel algorithm), in order to create a listening experience as ecological as possible. Within this frame, an important experimental protocol is designed in order to measure the perceived annoyance caused by each synthesized sound scene and to control the cognitive load (attention on a list of words to play back afterwards) of the participants. The first rounds of data analysis show consistent results, especially in terms of influence of sound level or source typicality on the measured annoyance. A deeper round of analysis is currently being processed in order to examine the fine correlation between measured and predicted annoyance values, with regards to psychoacoustic features of source signals. All the results will be presented and discussed in the course of the conference.

Introduction

The present study comes within the broad scope of environmental acoustics and community noise research and management, especially referenced by the seminal work of R.M. Schafer and colleagues' World Soundscape Project [START_REF] Schafer | The tuning of the world[END_REF]. It is guided by the 2002/49/CE European directive [2] that aims at establishing strategic soundmaps and encourages -or forces -the politics to address issues concerning noise exposure and pollution. It stands in a large-scale research program (Mouvie, 2014-17) that deals with relationships between mobility and life quality in urban environments; and makes the assumption that road traffic is the 1 st -order source of pollution in cities, either for air quality or noise exposure -we will focus here on the latter (acoustic) part of this working hypothesis [START_REF] Misdariis | Mobility and life quality relationships-Measurement and perception of noise in urban context[END_REF]. ICSV26, Montreal, [START_REF] Brocolini | Caractérisation de l'environnement sonore urbain: Proposition de nouveaux indicateurs de qualité[END_REF] This being, and quoting Chételat [START_REF] Chételat | La figuration cartographique de l'espace sonore. Images Re-vues[END_REF], the urban noise pollution issue could be addressed either in a " normative " way -based on objective measurements, sound level thresholds and operational solutions (noises barriers, soundproofing, etc) -, or in a " sensitive " way -based on subjective metrics, features related to psychoacoustical, cognitive or even social components enabling a more detailed description of the noise exposure and a better understanding of its underlying impacts. Actually, from the last decades, the need for developing new methodologies to qualify subjective approaches of environmental soundscapes has been formalized [START_REF] Schulte-Fortkamp | Soundscape-focusing on resources[END_REF] and numerous attempts have been made for modeling such environments with perceptual parameters. Among others, [START_REF] Kang | Urban sound environment[END_REF] lists several factors of different natures (physical, psychological, social, etc.) that are involved in the soundscape assessment [START_REF] Kang | Urban sound environment[END_REF], and [START_REF] Brocolini | Caractérisation de l'environnement sonore urbain: Proposition de nouveaux indicateurs de qualité[END_REF] proposes new sound features to characterize the perceived quality of urban soundscapes [START_REF] Brocolini | Caractérisation de l'environnement sonore urbain: Proposition de nouveaux indicateurs de qualité[END_REF]8]. More recently, and still among others, the European project HARMONICA (2011)(2012)(2013)(2014)(2015) proposes a new perceptual metric, the Harmonica Index, based on a combination of different energetic features, and the french collaborative research project EUREQUA (2012-2017) aims at identifying relevant criteria related to characterization of the physical environment (acoustics, but also air quality or climate) and evaluation of the quality of life in the neighborhood by residents and users [START_REF] Gauvreau | Environmental quality at district scale: A transdisciplinary approach within the EUREQUA project[END_REF].

Within the Mouvie program, a research has been conducted to design a tool able to diagnose noise annoyance perceived by citizens in a given context [START_REF] Leiba | Conception d'un outil de diagnostic de la gêne sonore en milieu urbain[END_REF]. Basically, this work relies on 3 main phases:

1. measurement of urban sound scenes to characterize single sound sources of passing-by road vehicles. This is done with a multi-channel device (Megamicros), coupled with a video tracking system. This allows to localize moving sources and extract their acoustic contributions [START_REF] Leiba | Urban acoustic imaging: from measurement to the soundscape perception evaluation[END_REF]. 2. classification of sound events into 7 perceptually coherent categories according to: a) type of vehicles (light, heavy, 2-wheels), b) driving conditions (acceleration, deceleration, constant speed) [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF]. This results in single source automatic labeling (overall estimation error 15%) [START_REF] Leiba | Large array of microphones for the automatic recognition of acoustic sources in urban environment[END_REF]. 3. modeling of noise annoyance, on the basis of Morel et al.'s perceptual model using acoustic / psychoacoustic features for each class-specific annoyance [START_REF] Morel | Noise annoyance assessment of various urban road vehicle passby noises in isolation and combined with industrial noise: A laboratory study[END_REF]. This model has been refined during the current work, especially with regards to some psychoacoustic features. The last step of this 3 rd phase concerns the evaluation of the model and forms the core of the present paper. It basically consists in comparing computed (model) and measured (experiment) data, in order to confirm the accuracy of the modeling, and if needed, to adjust the model to the new data set. For that, we implement a laboratory protocol based on a virtual audio environment, and conducted a perceptual experiment that allows lay participants to express their feeling about annoyance of semi-virtual sound scenes, controlled in terms of context (urban noise), composition (vehicle category) and sound level.

After briefly presenting the perceptual noise annoyance model inherited from the literature and its improvements (Sec. 2), the paper largely details the experimental approach (Sec. 3.1) and develops the main significant results obtained according to the evaluation challenge (Sec. 3.2), trying to resolve this fundamental issue: how can we predict annoyance felt by people exposed to a complex urban sound scene, on the basis of the fine knowledge on the content of the scene ? The conclusion then gives the main perspectives for future works in that direction.

Perceptual noise annoyance model

The current perceptual noise annoyance model is based on a multi-linear regression of acoustic and psychoacoustic features computed from the signal, i.e. a vehicle sound signature. At this point, it is admitted that this 1 st -order approach doesn't take into account non negligible components of perceived annoyance like, for instance, social, demographic or cultural inputs (see Berglund

Initial state

The model comes from Morel et al. work that established relationships between acoustic features and annoyance for the perceptual vehicle categories [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF]. For each of the categories (Cn), a specific annoyance (A n ), is computed by a multi-linear regression model that significantly explains (R 2 scores between .91 and .97, p <0.05) the variation of perceptual data obtained experimentally [START_REF] Morel | Noise annoyance assessment of various urban road vehicle passby noises in isolation and combined with industrial noise: A laboratory study[END_REF] (Table 1). [START_REF] Morel | Road traffic in urban areas: A perceptual and cognitive typology of pass-by noises[END_REF] and specific annoyance models (A n ) [START_REF] Morel | Noise annoyance assessment of various urban road vehicle passby noises in isolation and combined with industrial noise: A laboratory study[END_REF] C1: (2-wheel vehicles, constant speed) where N: overall loudness (mean); N 15-18 : specific loudness for Barks 15 to 18 (mean); R max : roughness (max); F, F max : fluctuation strength (mean, max); ΔN -: loudness decreasing rate (after vehicle passing); L MF : A-weighted level in the medium frequency range (315 -1250 Hz, 1/3 octave).

Current refinement

The initial model brings out the recurrent contribution of important psychoacoustic features. Loudness N is used in all categories except C6 (light vehic. accel.), and loudness decreasing rate ΔN -is also used in C3 (light/heavy vehicles const. speed). Roughness R is used in C4 (2-wheel vehic. decel.). Fluctuation strength F is used in C2, C5 and C7 (respectively, 2-wheel vehic. accel., light/heavy vehic. decel. and heavy vehic. accel.). Then, the current improvement of the model mainly concerns these three features that are usually difficult to derive for complex sounds and even somewhat ill-defined.

In their study, Morel et al. used dBSonic software (from 01dB Metravib) to compute all the audio features. But this tool is no longer available. It led us to base our work on open-access implementations of the different index models. In this study, loudness is computed with Fastl and Zwicker's instationnary loudness model [START_REF] Fastl | Psychoacoustics: facts and models[END_REF], implemented by Genesis (Loudness Toolbox). For roughness index, an optimized version of Garcia's implementation [START_REF] García | Modelling the sensation of fluctuation strength[END_REF] of Daniel and Weber's model [START_REF] Daniel | Psychoacoustical roughness: Implementation of an optimized model[END_REF] is used. The optimization helps in better fitting the theoretical curves established by Fastl and Zwicker [START_REF] Fastl | Psychoacoustics: facts and models[END_REF]. The fluctuation strength model used is implemented from Osses Vechhi et al. [21]. Also for this index, an optimization of the implementation is performed to better assess this auditory percept.

Experimental evaluation of the perceptual noise annoyance model

The assessment of the noise annoyance perceptual model presented in Sec. 2 forms the core of the current work. It aims at giving a final evaluation of the 3-phase research process described in the Introduction. The present section first describes the experimental method used to implement this goal (Sec. 3.1), before delivering a series of results regarding successively the consistency of participants' answer, an indirect way to estimate annoyance and, finally, the accuracy of the model (Sec. 3.2). Note that the methodological point related to ecological validity and realism of the experimental audio rendering setup has been left aside, since it is largely developed in a complementary paper [START_REF] Misdariis | Community Noise Quality Assessment (Annoyance) by means of a Virtual Audio Environment[END_REF].

Method

Stimuli

By analogy with soundscape structural composition [START_REF] Schafer | The tuning of the world[END_REF], the experimental stimuli are designed as 2component sequences mixing an urban background noise (texture) and vehicle sounds (events).

Background noise comes from a database of field recordings in Parisian outdoor spaces realized by two professional sound engineers for a previous work [23]. They cover a large scope of atmospheres, from calm zones (narrow lanes, quiet zones, etc.) to very noisy areas (large avenues, with a high traffic flow, etc.). The recording is made, among other devices, with an ORTF couple (Schoeps MSTC 64U).

Sound events come from a sound database realized in the Mouvie framework [START_REF] Leiba | Conception d'un outil de diagnostic de la gêne sonore en milieu urbain[END_REF][START_REF] Leiba | Urban acoustic imaging: from measurement to the soundscape perception evaluation[END_REF]. They were made during a campaign that recorded separately different vehicles with controlled conditions: vehicle types (light/heavy/2-wheel), engine types (number of cylinders, engine size), dynamics (acceleration, deceleration, constant speed) and general context (an automotive professional test track). The recording is made, among other devices, with a monophonic microphone (B&K 4190).

On this basis, sound materials for the experiment is designed in 2 steps. Recordings of urban backgrounds are edited to create an amorphous sequence [START_REF] Maffiolo | De la caractérisation sémantique et acoustique de la qualité sonore de l'environnement urbain[END_REF][START_REF] Dubois | A cognitive approach to urban soundscapes: Using verbal data to access everyday life auditory categories[END_REF]. Monophonic recordings of vehicle sounds are selected to illustrate the 7 perceptual categories (Table 1).

Apparatus

On the basis of seminal studies dealing with realism of multi-channel audio rendering devices, we choose to use a 5.1 broadcast system as a compromise between ecological and immersive properties [START_REF] Guastavino | Perceptual evaluation of multi-dimensional spatial audio reproduction[END_REF]. The set-up is installed in a mastering professional studio by means of a 5.1 PSI monitoring system (see [START_REF] Misdariis | Community Noise Quality Assessment (Annoyance) by means of a Virtual Audio Environment[END_REF] for details). Participants were seated on a chair, respecting the 5.1 disposition of the studio and they faced a computer on which the instructions of the experiment were displayed.

The software Panoramix [START_REF] Carpentier | Panoramix: 3D mixing and post-production workstation[END_REF] allows to encode the two groups of stimuli (background and events) in the 5.1 format, ensuring diffused spatialization of the background and precise localization of the events.

Participants

Thirty-six people (17 women) participated in this experiment (Age: 19-48 years old, Mean: 26). All declared having normal hearing abilities, no neurological impairment and no attention deficit disorder. They were paid for their participation. They were separated into two groups: group (a) only assessed noise annoyance and group (b) assessed annoyance "indirectly" (i.e., they performed a cognitive task while being exposed to the stimuli and rated their workload in addition to the perceived annoyance). For both groups, the experiment lasted for about 50 minutes.

Procedure

The experimental procedure is mainly inspired by two previous studies dealing with noise annoyance measurement of industrial or environmental sounds [START_REF] Morel | Noise annoyance assessment of various urban road vehicle passby noises in isolation and combined with industrial noise: A laboratory study[END_REF][START_REF] Brocolini | Effect of masking noise on cognitive performance and annoyance in open plan offices[END_REF]. An experimental plan with repeated measurements has been designed with 3 independent variables (vehicle category, sound level and participant's group) and 3 dependent variables (annoyance, memory performance and workload).

Twenty-height sound sequences lasted for about 25 seconds were designed for both groups, with 3 events of the same vehicle category and at the same sound level -among 4 different levels (from 50 to 62 dB(A) by 4-dB step). Events appeared in a random order within each sequence, as each sequence. In addition, 4 sequences with only background noise were inserted at specific moments of the experiment (#6, 13, 19 and 26) and served as control conditions. In both groups, the experiment began with a training test to ensure the correct understanding of the task.

Transposing the method adopted by [START_REF] Morel | Noise annoyance assessment of various urban road vehicle passby noises in isolation and combined with industrial noise: A laboratory study[END_REF], participants of group (a) were firstly asked to imagine themselves doing an activity of their choice requiring concentration (e.g. reading, writing an e-mail, etc. …) at a coffee terrace. Then, for each sound sequence, participants answered the following question: "At which point did the sound sources annoy you in accomplishing the task you imagined doing? " [START_REF] Morel | Noise annoyance assessment of various urban road vehicle passby noises in isolation and combined with industrial noise: A laboratory study[END_REF]. They answered on a continuous scale (from 0 to 10), marked by 5 labels from "not at all" to "extremely" annoyed. Group (a) assessed each sound sequence twice: a 5-minute break was taken between block 1 and 2 that contained each the whole set of 32 sequences. Participants could take as much time as they wanted to answer each question.

Group (b) assessed the annoyance of each sequence while performing a working memory task [START_REF] Brocolini | Effect of masking noise on cognitive performance and annoyance in open plan offices[END_REF]. For each sequence, participants were instructed to memorize the 10 words that were displayed on the computer. As in [START_REF] Brocolini | Effect of masking noise on cognitive performance and annoyance in open plan offices[END_REF], we chose words from the lexical lists established by [START_REF] Dubois | Normes catégorielles » et listes lexicales[END_REF] [START_REF] Dubois | Normes catégorielles » et listes lexicales[END_REF]. We selected 16 lists (such as Sports, Trees or Fruits), of 20 frequently used words. Lists and words in lists were randomized for each participant. Each list appeared twice (with 2 different 10-word sets), so that each participant had finally seen all words of all lists.

The experiment was conducted as follows: first, participants did a Stroop task that is used to measure their capacity to inhibit interference effect (here, the noise environment) while performing a task [START_REF] Kostallari | Irrelevant speech effect in open plan offices: a laboratory study[END_REF]. Afterwards, for each sequence, a fixation cross appeared in the middle of the computer screen during 1,5s (to guide the participant's gaze), then the lexical list's name was displayed during 2s, and again a fixation cross appeared during 1,5s. After that, the 10 lexical list's words were displayed successively for 2s each. Participants were asked to wait 5s before speaking (i.e., to let the vehicle pass-by noises end). Then, they had 15s to return orally as many words memorized as possible, knowing that each word recalled correctly earned 1 point, and each word recalled wrongly removed 1 point.

Furthermore, they completed a French version of the NASA-RTLX (Raw Task Load Index) questionnaire [START_REF] Brocolini | Effect of masking noise on cognitive performance and annoyance in open plan offices[END_REF], which measures the level of workload using 6 aspects (i.e., mental activity, provided effort, frustration, etc.). They also assessed noise annoyance through the following question: "At which point did the sound sources affect you in accomplishing the task requested?". The 6 NASA-RTLX questions and the noise annoyance question were mixed and randomized for each sound sequence. To answer each question, participants could take as much time as they wanted and they had to place the cursor of the computer mouse on the scales -as it has been explained about group (a), for annoyance scale.

Regarding noise environment, the background noise was emitted continuously at 45 dB(A) during the experiment. The 3 pass-by noises of each sequence appeared after the second fixation cross. During the participant's restitution and completion of questionnaires, only the background noise was diffused.

Results

We introduce here the first outcomes of this experiment: annoyance variability (Sec. 3.2.1), relationship between annoyance and performance (Sec. 3.2.2), and model accuracy (Sec. 3.2.3).

Noise annoyance evaluation -group comparison

The annoyance rates within the groups (a) and (b) are averaged over all the participants for each of the 28 combinations of vehicle category vs. noise level. Figure 1 shows the averaged annoyance rate of group (a) with regards to group (b). A linear relation between the two set of rates can be noticed. We obtain a linear regression with a correlation coefficient of 0.88 (p<.001) and a well-spread distribution along this line, meaning that the average of annoyance judgment are significantly similar. These results address the issue of annoyance definition and its difference to unpleasantness. Indeed, whereas annoyance is commonly linked to an action [START_REF] Guski | The concept of noise annoyance: how international experts see it[END_REF], our data seem to contradict this point of view and show a similar percept, independent of the listener's state (passive or active).

Performance as a good annoyance index

In group (b) the words recall are used to compute participants' performances. Raw performance (number of good words minus bad words) is normalized by the average performance during the 4 sequences without vehicle events. Figure 2 shows perceived annoyance vs. normalized performance, averaged over the participants, for each of the 28 audio combinations. Data show a certain correlation between these two dimensions. The plain regression line is obtained for the entire dataset. It gives a correlation coefficient of -0.52, which is not entirely satisfying. We can notice that vehicles in acceleration (C2, 6, 7) passing-by at 62 dB(A) form a cluster of "outlier" data. The dotted regression line is obtained for the dataset without these combinations. It gives a better correlation coefficient of -0.71 (p<0.05). 2), we confront its outcomes with our experimental data, (group (a)). Results are presented in Figure 3. They show a significant correlation (p<10 -3 ) between the model and the experiments with a coefficient r = 0.89. We can notice that the major part of the categories is well-spread around the regression line. After all, two of them (C2, 6) seem to deviate from it. The model could be adapted for these two categories by modifying the coefficient governing the dependence on loudness (for C2) and the sound level in medium frequency (for C6). Finally, a global multiplicative correction coefficient of 0.58 seems to be needed to re-scale Morel et al's model.

Conclusion -Perspectives

This paper presents the whole frame of a large scale research program regarding the characterization of urban soundscapes, according to road traffic sources. It focuses on the last part of the program that implements an experimental validation of a perceptual noise annoyance model.

The experimental procedure is precisely described, in addition to a complementary paper that deeper addresses the audio rendering ecology issue [START_REF] Misdariis | Community Noise Quality Assessment (Annoyance) by means of a Virtual Audio Environment[END_REF]. The results obtained show a good consensus between two groups of participants: (a) passive assessment, (b) active assessment with a cognitive task during the listening test. This may lead to challenge the common distinction between annoyance and unpleasantness. This being, the results also find out that a cognitive task performance measurement (e.g., memory) can be used, in a certain range of sources, to predict the annoyance percept. Finally, and most importantly, the results validate, to some degree, the initial perceptual model of annoyance established by Morel et al. [START_REF] Morel | Noise annoyance assessment of various urban road vehicle passby noises in isolation and combined with industrial noise: A laboratory study[END_REF], and give some cues to improve it.

Perspectives of this work are the following: i) to consolidate the results with complementary analysis and/or additional experiments; ii) to improve the model, regarding either psychoacoustic features refinement or global annoyance implementation. The latter point leads to an important -an yet unresolved -issue regarding the summation process that occurs with a complex mixture of sound events.

A 1 = 1 .

 11 03*N + 0.18 C2: (2-wheel vehicles, acceleration) A 2 = 16.99*N 15-18 + 0.10*F + 1.45 C3: (light+heavy vehicles, constant speed) A 3 = 1.32*N -0.32*ΔN --0.36 C4: (2-wheel vehicles, deceleration) A 4 = 0.89*N + 0.02*R max + 0.33 C5: (light+heavy vehicles, deceleration) A 5 = 1.07*N + 0.08*F max -1 C6: (light vehicles, acceleration) A 6 = 0.29*L MF -8.5 C7: (heavy vehicles, acceleration) A 7 = 0.95*N + 0.10*F -0.5

Figure 1 :

 1 Figure 1: Linear regression between mean annoyance assessed in group (a) vs. group (b). Each point is a vehicle category (1 to 7) at a given sound level (50 to 62 dB). Pearson's coefficient correlation is 0.88, (p <.001)

Figure 2 :

 2 Figure 2: Linear regression between mean annoyance and performance data. Each point is a combination of vehicle category vs. noise level. Linear regression computed with the whole dataset gives -0.52 for Pearson's coefficient (plain line); without C2, 6 and 7 Pearson's coefficient reaches -0.71 (dotted line) 3.2.3 Estimated vs. measured annoyance Using Morel et al.'s implementation (Sect.2), we confront its outcomes with our experimental data, (group (a)). Results are presented in Figure3. They show a significant correlation (p<10 -3 ) between the model and the experiments with a coefficient r = 0.89. We can notice that the major part of the categories is well-spread around the regression line. After all, two of them (C2, 6) seem to deviate from it.

Figure 3 :

 3 Figure 3: Linear regression between estimated annoyance and experimentally measured annoyance (group (a)). Each point corresponds to a combination of vehicle category (1 to 7) emitted at a specific noise level (50 to 62 dB(A)). For measured annoyance, each point represents the mean annoyance over all the participants, for each combination. Estimated annoyance is obtained by averaging the specific annoyance rate given by the model for each vehicle (three by sequence). Pearson's coefficient gives a significant correlation of 0.89, (p < 0,001).

  et al. for general notions [16] or de Coensel et al. for a specific instance of such a cognitive multi-factorial model [17l]).

	ICSV26, Montreal, 7-11 July 2019	3

Table 1 :

 1 Morel et al.'s perceptual road traffic categories (Cn)

Brocolini, L., Lavandier, C., Marquis-Favre, C., Quoy, M., & Lavandier, M. (2012, April). Prediction and explanation of sound quality indicators by multiple linear regressions and artificial neural networks. In Acoustics 2012

Mietlicki, C., Mietlicki, F., Ribeiro, C., Gaudibert, P., & Vincent, B. (2014, September). The HARMONICA project, new tools to assess environmental noise and better inform the public. In Proceedings of the Forum Acusticum Conference, Krakow, Poland (pp. 7-12).

Acknowledgements

This work 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).


V Le Clerc

C Aubert²

V Cottet

C Yovanopoulos

M Piquet

A Suel

S Huet

C Koutouan

L Hamama

G Chalot²

M Jost²

B Pumo

M Briard

Resistant carrots to Alternaria dauci or tasty carrots: should we

Keywords: root quality, bitterness, metabolite QTL, leaf blight, Daucus carota

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

While breeding for varieties highly resistant to Alternaria dauci is a top priority for carrot seed companies, consumers are more and more demanding on gustative quality, especially low bitter carrots. Amon others, terpens and polyacetylenes are considered key compounds for bitterness in carrots [START_REF] Simon | Correlations between sensory and objective parameters of carrot flavor[END_REF]Seljasen et al. 2001). Relying on sensory and chemical analyses, [START_REF] Kreutzmann | Prediction of sensory quality in raw carrots (daucus carota L.) using multi-block LS-ParPLS[END_REF] highlighted that a small number of metabolites, among them eight terpens, could predict bitterness. However, while the genetic control of carrot resistance to A. dauci has already been investigated, leading to the identification of rQTLs (Quantitative Trait Loci involved in resistance) in different genetic backgrounds [START_REF] Clerc | Genetic architecture of factors underlying partial resistance to Alternaria leaf blight in carrot[END_REF][START_REF] Clerc | QTL mapping of carrot resistance to leaf blight with connected populations: stability across years and consequences for breeding[END_REF], no information were available except very recently published by [START_REF] Keilwagen | The terpene synthase gene family of carrot (Daucus carota L.): identification of QTLs and candidate genes associated with terpenoid volatile compounds[END_REF] on terpens and polyacetylenes. Identifying the genomic regions involved in resistance and/or bitterness will be very helpful for breeders to develop resistant varieties while avoiding increasing bitterness. To answer this question, chemical and sensory analyses were performed on a set of five carrot genotypes during three years, in four environments to identify the terpens involved in bitterness. These metabolites were characterized during two consecutive years in field trials in a population already segregating for resistance to Alternaria dauci and their genetic control was determined. The colocalization between previously identified rQTLS and mQTLS (QTLs involved in the accumulation of metabolites) were examined and recommendations were made for breeders.

MATERIALS AND METHODS

Plant material and experimental trials

During three consecutive years, two parental lines -H1 (susceptible to Alternaria dauci) and K3 (partially resistant) -of a segregating population named PC3; I2, another line partially resistant to A. dauci and two commercial hybrids (Boléro and Presto) were sown under a tunnel in Angers (Maine et Loire, France) and in the field in Lanxade (Dordogne, France) in 2014; in Field in Blagon (Gironde, France) and in Ychoux (Les Landes, France) in 2015 and 2016, respectively. For each trial, 180 seeds per genotype were sown on two meters in randomized blocks in duplicate as well as for the ~150 F2:3 lines of the segregating population PC3 evaluated in 2015 and 2016 in the same fields. While in the tunnel plants were inoculated with an A. dauci strain (P2), the infection occurred naturally in the field.

Chemical and sensory analyses

For chemical analyses, 15 to 20 plants per biological replicate and genotype were harvested at maturity and the central portions of the roots were shredded in a food processor, immediately frozen in liquid nitrogen and stored at -80 °C until analysis. The terpens and polyacetylenes were separated and characterized on a Perkin Altus BEH C18 column (50 mm x 2.1 mm, i.d. 1.7 μm) using a Waters Acquity UPLC® system equipped with a photodiode array detector (Acquity PDA e).

For sensory analyses, 100 homogeneous and representative roots of each biological replication per genotype were pooled into one lot for the sensory panel in 2014 while in 2015 and 2016, the two biological replications were kept separated in 2015 and 2016. The panel was composed by more than 12 people specifically trained on carrot attributes including two attributes of basic taste (sweet, bitter) [START_REF] Cottet | Description sensorielle de la carotte[END_REF]. The sensory results of the five genotypes were then compared with the biochemical results to identify the metabolites likely to predict bitterness.

Output data

Principal component analysis (PCA) was performed to describe the correlation between bitterness and volatile compounds in the set of five genotypes evaluated in four environments with R software version 3.3.2. Analyses were carried out with average biochemical values per year and scores of bitterness per year. The coefficients of variation were calculated from bitterness and disease means.

The accumulation value of each metabolite from each genotype replicate per year was autoscaled (i.e. centered-scaled, as described by van den Berg et al. 2006), according to the mean and standard deviation of the accumulation of the metabolite concerned per year. Thereafter, average data from autoscaled values wereused to detect mQTLs in PC3 population, as previously described for rQTL analysis in Le [START_REF] Clerc | QTL mapping of carrot resistance to leaf blight with connected populations: stability across years and consequences for breeding[END_REF]. rQTLs had already been identified on a consensus map (Le [START_REF] Clerc | QTL mapping of carrot resistance to leaf blight with connected populations: stability across years and consequences for breeding[END_REF]. However as new SSRs were added, rQTL detection was repeated to check the positions of the rQTLs on a new map to compare mQTL and rQTL positions.

RESULTS AND DISCUSSION

According to the sensory analysis of the five genotypes in the four environments, the range of bitterness values (min-max) was similar across environments and years and, genotype K3 was consistently the most bitter one while H1 and Presto the least bitter ones (Table 1). Seventeen terpenes and three polyacetylenes compounds were detected in common from the biochemical analysis over all three 2014-to-2016 years. 1 Mean disease score based on a 0-9 scale with 0 corresponding to symptomless plants and 9 to plants that were totally blighted 2 CV: coefficient of variation 3 : Mean were adjusted according to the number of biological repetitions per year

PCA components 1 and 2 explained 68% of the variance in the analysis of the five genotypes. Component 1 best described bitterness, with correlation values of 0.79 and the ten metabolites with correlation values greater than 0.6 (p.value <= 0.01) for component 1 were considered as good predictors of bitterness: limonene, sabinene, α-terpinolene, βpinene, falcarindiol, β-caryophyllene, myrcene, falcarindiol acetate, p-cymene-8-ol and bornyl acetate (Table 2). Our results confirmed that some terpenes and polyacetylenes are good predictors of bitterness as highlighted by [START_REF] Kreutzmann | Prediction of sensory quality in raw carrots (daucus carota L.) using multi-block LS-ParPLS[END_REF] who also identified falcarindiol, terpinolene, caryophyllene, β-pinene and sabinene among these predictors. We identified 25 mQTLs, with one to three mQTLs per metabolite (Table 3). No mQTL was detected for p-cymen-8-ol. The part of the phenotypic variation explained by all the mQTLs identified for one metabolite was generally high, ranging from 12% for bornyl acetate to 58% for falcarindiol and falcarindiol acetate, with a mean of 37 %. Among all the metabolites involved in bitterness, the two polyacetylenes presented the highest number of mQTLs with the highest total R² value for falcarindiol (~58%). The biggest mQTL for falcarindiol was identified on chromosome 9 (Figure 1). To date no rQTL has been identified on this chromosome, that counter-selecting this genomic region could reduce the perception of bitterness without compromising resistance.

While mQTLs were identified on eight chromosomes, a hotspot of mQTLs was identified on chromosome 4 representing 9 out of the 25 mQTLs. Table 3. mQTLs detected for the metabolites predictors of bitterness, identified in a segregating carrot population evaluated in 2015 and 2016 in the field.

Metabolite

Chr The upper and lower parts of this chromosome 4 presenting six and three mQTLs respectively colocalized with an rQTL whose favorable allele for resistance came from the K3 parental line (Le [START_REF] Clerc | QTL mapping of carrot resistance to leaf blight with connected populations: stability across years and consequences for breeding[END_REF]. Whatever the metabolite with an mQTL identified in this hotspot (except for the falcarindiol acetate), the alleles leading to higher contents were also supplied by the resistant K3 line. Therefore, when trying to increase resistance to Alternaria dauci, breeders should avoid this region because bitterness would also probably be increased. As explained by [START_REF] Carreno-Quintero | Untargeted Metabolic Quantitative Trait Loci Analyses Reveal a Relationship between Primary Metabolism and Potato Tuber Quality[END_REF] 6 , colocalization of QTLs related to different traits may indicate a common regulator that controls the variation of both traits, or the physical proximity of genes involved in different biological processes. The three mQTLs on chromosome 2 are also partially colocalizing with an rQTL. However, these colocalizations result in favorable combinations of resistance and bitterness alleles in genotype H1 suggesting that breeder could increase resistance by selecting the rQTL of H1 on this chromosome while decreasing bitterness. Breeders could also select the biggest rQTL on chromosome 6 (R²~20%) as only one mQTL for bornyl acetate (R²~12%) is colocalizing and a law level of bitterness is expected in carrot as part of the typical carrot flavor [START_REF] Simon | Carrot flavor: effects of genotype, growing conditions, storage, and processing[END_REF]. For the other rQTLs on chromosomes 1, 5 and 8, they can used to increase resistance without problem as no mQTL involved in bitterness are colocalizing.

CONCLUSION

In conclusion, breeding for resistance to Alternaria dauci without affecting root quality, i.e. without increasing bitterness is possible provided they select the favorable alleles for each trait.

-First study of the genetic determinism of carrot polyacetylenes has been done in the present study.

-Compared to [START_REF] Keilwagen | The terpene synthase gene family of carrot (Daucus carota L.): identification of QTLs and candidate genes associated with terpenoid volatile compounds[END_REF], new mQTLs for terpenes have been identified and map on the carrot genome.

-Breeders can rely on rQTLs on chromosomes 1, 5, 8, 6 and 2 to increase resistance while decreasing bitterness under the conditions mentioned above.

Figure 1 .

 1 Figure 1. Resistance QTLs (RL) common to two or three years and RL11, RL14 and RL16 identified in 2011, 2014 and 2016 respectively identified only one year. RL are represented with 1-LOD support interval (solid bar) and R² in percentage and mQTLs of metabolites predictors of bitterness on the carrot chromosomes. For mQTL, maximal position is indicated by the circle and size of the circle is proportionnal to R². Code of the metabolites: ater= α-terpinolene; ba= bornyl acetate; lim= limonene; sab= sabinene; FAA= acetate; bcar= βcaryophyllene; FAD= falcarindiol; bpi= β-pinene; myr=myrcene.

Table 1 .

 1 Disease score (Alternaria dauci), bitterness score end their coefficient of variation of five carrot genotypes evaluated in four environments.

	Genotype	Varietal	Disease score 1			Bitterness
		type	Mean	CV 2	2014		2015 2016 Adjusted	CV 2
									mean 3
					Tunnel Field Field Field
	K3	S2 line	2.2	0.52	6.3	7.6	3.0	5.2	5.0	0.36
	I2	S2 line	4.1	0.04	5.5	6.0	1.3	3.5	3.5	0.57
	H1	S3 line	7	0.22	0.8	1.6	0.3	0.9	0.8	0.58
	Bolero	Hybrid	4	0.26	1.4	2.3	0.9	3.9	2.2	0.64
	Presto	Hybrid	7.9	0.11	3.1	0.9	0.5	1.0	1.2	0.82
	Mean bitterness score/environment		4.4	5.3	1.9	3.7	3.5

Table 2 .

 2 Correlations between component one and bitterness (in bold) and correlations (>0.60) between component one and metabolite variables from PCA of the five genotypes in four environments.

	Type of metabolite	Attribute and metabolites	Correlation with component 1	p.value
		Bitterness	0.792	0
	Monoterpene	Limonene	0.952	0
	Monoterpene	Sabinene	0.948	0
	Monoterpene	α-terpinolene	0.912	0
	Monoterpene	β-pinene	0.907	0
	Polyacetylene	Falcarindiol	0.866	0
	Sesquiterpene	β-caryophyllene	0.838	0
	Monoterpene	Myrcene	0.804	0
	Polyacetylene	Falcarindiol acetate	0.804	0
	Monoterpene	p-cymen-8-ol	0.790	0
	Monoterpene	Bornyl acetate	0.642	0.002

ACKNOLEDGMENTS

The authors would like to thank the IRHS-Imorphen team involved in tunnel experiments in Angers, Emilie Clavel and François Villeneuve for the field trial in Ctifl (Lanxade) and Stéphan Plas (Invénio) for the field experiment in Les Landes. This work is part of the research program AlterQual financed by the Ministry of Agriculture (FranceAgriMer).


Nicolas Misdariis
email: nicolas.misdariis@ircam.fr

Andrea Cera
email: andreawax@yahoo.it2william.rodriguez@renault.com

William Rodriguez

Electric and Autonomous Vehicle: from Sound Quality to Innovative Sound Design

Keywords: Sound design, Human-Machine Interface, Autonomous vehicle

This study follows long-term lines of research and realizations accomplished within the frame of electric vehicle sound design. It currently deals with the most up-to-date issue addressing the near future mobility: Autonomous Drive (AD). It has been implemented in a 2-year collaboration with a french car manufacturer that deployed a high-tech demo-car where the sonic dimension was explored in a spatial and a multisensory approach. Specific hardware devices (multi-channel diffusion system) and software protocols (spatialization and vehicle data transmission) made possible the study and realization of numerous and accurate internal/external warning sounds and auditory Human-Machine Interfaces. The autonomous driving condition was especially considered as it corresponds to an unprecedented user experience in terms of human-centered process, where sounds could play a crucial role for informing and, if necessary, reassuring the driver and passengers. This paper presents a thorough analysis of the project: initial requirements and data, multi-disciplinary framework, technical details, sound concepts and realization. It also opens onto reflexive and prospective considerations about the global notion of sound quality that especially look at possible forms of low-intrusiveness in sound design, and their related properties.

INTRODUCTION

The project upon which the present paper is based, comes within the broader frame of sound design for automotive industry. In fact, the vehicle cockpit is a place -and the driving situation is a moment -where a large number of intentional informations (see Langeveld et al. [START_REF] Langeveld | Product sound design: Intentional and consequential sounds[END_REF] for the concept of "intentional" sound) is given to the driver -and incidentally, to the passengers -at different levels of significance [START_REF] Suied | Toward a sound design methodology: Application to electronic automotive sound[END_REF]: from very urgent (warning of a sudden dysfunction, for instance the temperature increase in the cooling circuit), to just informative (notification of a standard action, for instance the blinker triggering). These functional informations are mainly transmitted by two sensory modalities: vision (lights and icons on the dashboard) and hearing (sounds playing the role of auditory displays, or what can be alternatively called sonic Human-Machine Interfaces -HMI); and, this is without considering the vibro-tactile solutions that strongly tends to emerge …. [START_REF] Brewster | Tactons: structured tactile messages for non-visual information display[END_REF][START_REF] Kim | Vibrotactile display for driving safety information[END_REF]. This being, the recent developments of new vehicles types (smart-connected and/or electric-silent vehicles) and new driving configurations (in particular, the different levels of autonomous drive) lead to reinforce the dialogue between Human (driver and passengers) and Machine (car), and therefore, the need for relevant, efficient, coherent and, a fortiori, pleasant human-machine interfaces.

Within that frame, we recently contributed to an important and long-term industrial project (2016-2017) led by the car manufacturer Renault, that aimed at developing a demo-car 3 called Symbioz. This contribution involved our research team in sound perception and sound design (Ircam-STMS / SPD) and a collaboration with a composer/sound designer (Andrea Cera) with whom the team used to work for many years on that global topic [START_REF] Misdariis | Knowledge in Sound Design-The Silent Electric Vehicle: a Relevant Case Study[END_REF]. The unique specimen of Symbioz is an electric and autonomous car experimenting and integrating most of the up-to-date technologies available in the automotive industry, and especially the level 4 of autonomous drive assistance (see Sec. 2 for more details). In a user experience (UX) point of view, Symbioz represents a vision of the near future mobility (by around 2030): it is a tentative of fusion between a vehicle and a living space. The care for the passenger's comfort and peace of mind coexists with the experimentation of innovative technologies for controlling the car's behavior, internal atmosphere and feel. In that scope, the whole project has developed a multisense design approach involving research and applications in different design domains such as texture, color, light, sound, and even scent.

With regard to sound, the project offered two main sound design challenges: the design of an external VSP sound (Vehicle Sound for Pedestrians), and the design of a collection of sounds for the car's interior (cockpit sounds). The VSP comes in the standard issue addressed by the silence of electric vehicles that should nevertheless signify their presence [START_REF] Cocron | The silence of electric vehiclesblessing or curse[END_REF]. This part will not presently be presented as it is mainly based on results derived from previous projects assumed by the same research team (7). The present paper will focus on the internal cockpit sounds that really constituted a laboratory for studying the future of sonic HMI in the automotive context. We then designed innovative solutions considering two main directions -further detailed in the paper: • Exhaustiveness. We processed all passengers / cockpit interactions related to the three constitutive and explicit Symbioz's driving modes: Classic, Tonic and Autonomous. But more precisely and interestingly, the availability of the Autonomous driving mode created new design challenges, such as: how to communicate the levels of autonomy, or attention, of the car and its processing unit? How to reassure the passengers that everything is OK when the car is self-driving? • Spatiality. On the basis of the dedicated sound diffusion device implemented in the car, we developed an original concept of spatial sonic HMI. Every sound played in Symbioz's cockpit (blinkers, alarms, info sounds, …) is placed in a virtual acoustic space. In each driving mode, the sounds slightly move around virtual positions, with different degrees of jitter and reverberation. For instance, in Tonic driving, blinker sounds are firmly placed in the direction the car takes whereas in Autonomous driving, these same sounds are oriented much loosely, since no passenger focuses on driving. By moving in space, HMIs become less intrusive/annoying and more natural, as spatial changes translate into subtle timbral shifts (due to phasing and reflections in the cockpit's walls).

Then, as a challenging and original user context, the paper focuses on the Autonomous Drive (AD) sequence and furthermore develops all the ins and outs of the workflow: from the conceptual and multidisciplinary brainstorming of the role of sound in that context (sec. 2.1) to the description of the final designed sounds (sec. 2.4), going through presentation of the technical frame (Sec. 2.2) and the methodological process (Sec. 2.3). Then, in order to open this experience to discussion, we sum-up what we learned from the project (Sec. 3.1), considering it as a potential project-grounded research that implements a research-through-design approach (8) in sound design and that aims at contributing in sciences of sound design, a conceptual framework that we tend to progressively implement (Sec. 3.2). For that, we especially focus on the concept of "low-intrusiveness" (Sec. 3.3) that arose from this industrial collaboration and could be a strong and relevant guideline for future sound design contributions in that particular, or any other, applied domain.

FOCUS ON AUTONOMOUS DRIVE SEQUENCE

General Description and Sonic Challenge

As previously mentioned, the Symbioz demo-car gets three driving modes among which the Autonomous Drive (AD) represents an unprecedented user experience, and more broadly a future new life situation. It mainly consists in giving to the car the ability to make decisions concerning the drive without any human supervision or intervention. The level of autonomy implemented in Symbioz is at the standardized level 4 -also called mind off level - [START_REF]Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems[END_REF], ranked just after levels 2 and 3 (respectively hands off and eyes off), and before level 5 (steering wheel optional). This particular level 4 theoretically allows the driver to have another activity than driving, such as reading, working, moving a film or even sleeping! The kinds of decisions the car could then be able to take are, for instance, speed adjustment, vehicle overtaking, line change, urgent breaking and all the functional actions such as trigger/validation of the Autonomous Drive or return to a Manual Drive (actually, Classic mode). For that, the vision and the monitoring of the road and its vicinity is assumed to be 360° wide by means of a large number of sensors placed around or inside the car: frontal or angular radars, LIDARs systems (LIght Detection And Ranging), digital cameras, ultrasonic devices, etc. All the data captured by this exhaustive equipment are analysed by central processing units that are embedded in the car and aimed at defining the best course of actions for a safe and pleasant drive.

In that frame, the role of sound seems then naturally relevant, and even crucial. In fact, being Autonomous Drive an eyes off situation by default, visual informations are obviously no longer efficient to transmit informations about the vehicle state -and especially if the driver needs to regain control of the vehicle. Moreover, Autonomous Drive inherently involves the fact that driver and passengers must be fully confident about the brain capacities of the car in order to delegate their role without any apprehension. This cognitive mechanism is usually called re-assurance [START_REF] Beattie | A comparison of artificial driving sounds for automated vehicles[END_REF][START_REF] Kraus | Cognition and emotion in autonomous cars[END_REF]: it targets trustworthiness of the artificial intelligence responsible for driving and tends to evacuate all kinds of uncertainty and doubts in user's mind. Among other factors, re-assurance can be promoted by several cues of good functioning sent by the central processing system to the passengers, during the autonomous driving sequence. According to what we stated above, these cues must be sonic cues and should be as meaningful as possible to deliver the right information at the right moment, without being too intrusive nor annoying. All these latter issues form the global challenge of sound for electric/autonomous vehicles in general, and for Symbioz in particular.

Technical Frame

Relatively to the sound component, the Symbioz project rested upon a complex technological framework and apparatus. It is mainly based on a two-fold architecture mutually connected via a direct physical link (USB) and supplied by input parameters coming from the internal car network. The two parts of the system are: i) an encoded sound database stored in memory and an audio playback library running on the central system processor unit (Nvidia GPU) ; ii) a multichannel spatial decoding software attached to a 16-point hardware diffusion device implemented in the car (adapted from the Phantom technology by Devialet4 ). For any of the 60 sonic HMI of the sound collection designed for Symbioz -especially those concerning Autonomous Drive -, the synopsis of the technical protocol was the following: from an information sent by the central system concerning a current vehicle's state (e.g., battery level) or an action currently achieved in the cockpit (e.g., blinker), the corresponding encoded HMI is selected and sent to the multichannel component which decodes its features (level, loop size, etc.) and its spatial properties, then implement and play it on the hardware diffusion device via a proprietary virtual acoustic rendering tool.

The multi-loudspeaker system proposed by Devialet was inspired by the initial idea to make converge the inside of the vehicle with the living space (see above). It offers an original listening experience inside automotive cockpit by means of two dedicated hardware developments. First, miniaturized sound diffusion modules (inspired by the Phantom technology, adapted to low acoustic load volumes) replacing traditional in-door speakers, and located in the dashboard, B-pillars and behind the rear seats. Second, the "Diffusors", which are placed in empty spaces of the cockpit's structure and are connected to the modules in order to create a network of virtual loudspeakers and increase the degree of freedom in terms of spatialisation and immersion. With regard to this very specific sound apparatus and technology, the testing and tuning phases were done on a back-up car (with nearly the same interior volume and configuration than Symbioz) during the major part of the project. Because of the complex and highly multidisciplinary development of the demo-car, we only had at our disposal few workshops on a Symbioz mockup (Figure 1a) and two tuning sessions on the final release of the vehicle for an ultimate tuning phase before the official reveal (Figure 1b).

Sound Design Process

The development workflow of the 60-item sound collection mainly involved the design and specification of two main sonic dimensions: timbre (spectral content and temporal evolution), and space (localisation, spatial image and dynamics). A third basic dimension was also carefully taken into consideration during the testing and tuning phases: intensity, i.e. sound level at which the sonic HMIs should be diffused with regard to their functionality or context of use, and that directly relies on their saliency with regard to the background noise (see also Sec. 3.3).

Conception of timbres was done in the frame of the multisensory design approach mentioned above. It was inspired by the research work made by Superface design agency 5 on the materials used to fit cockpit's structure (doors, top) or elements (seats, dashboard). Moreover, several physical samples available in Superface's material database and involved in this design research (wood, aluminium, bakelite, etc.) served as primary sources of sound that were thoroughly recorded and used hereafter in different transforming/mixing processes to answer some insights of the overall design brief (authenticity, expertise, crafts, collective memory, etc.) and relationships with the other modalities, especially lights and colors.

Conception of spaces was also done in the same multisensory design frame and tried to answer another important part of this same brief that specified on how to "sculpt" the inner space of the cockpit. For that, we used the multichannel Devialet device in order to develop the concept of spatial sonic Human-Machine Interface. In fact, each HMI of the collection was placed in the 3D-space of the cockpit by defining its localisation (azimuth and elevation), its spatial image (width) and its dynamics (jitter), the latter describing how the spatial features evolves along time. Values and ranges of these parameters -together with the modeling of a virtual room acoustics of the cockpit -were fitted to the main character attached to each driving mode: Classic à neutral/natural, Tonic à focused/damped, and Autonomous à amplified/diffused (Figure 2) This being, this complex sound design process led us to develop ad-hoc tools for prototyping and harmonizing the whole set of sounds. First, an environment for realizing quick prototyping based on basic components (oscillators, broadband noise, filters) was especially built to sketch gross-categories of HMI's spectral contents and temporal morphologies; these sound mock-ups were then refined by applying different sound materials (resulting from the recorded samples mentioned above), by means of additive analysis/synthesis or cross-synthesis standard techniques. Second, a meta-scoring environment that gathered all the sound parameters used to design the HMIs was implemented in order to be able to organize -and harmonize -this large sound production. This kind of holistic approach was quite relevant to manage similarities (invariance) and differences (modulations) in terms of sound description (rhythm, pitch, consonance, noisiness, etc.) either from different groups of HMIs (e.g., Classic, Tonic and Autonomous), or from different HMIs in a same group (namely, Autonomous Drive). This finally helped to reach a certain consistency between or within pre-defined sound categories of the whole collection.

Sound Design Production

The Symbioz demo-car gets 10 sounds related to Autonomous Drive (AD) mode and its corresponding explicit functionalities: 'AD Available', 'AD Definitive', 'Hand Over Notification', 'Hand Over Required', 'Minimum Risk Maneuver', 'Back to Manual driving', 'Urgent braking', 'Line change', 'Trajectory change', 'Everything OK'.

These sound contains 3 types of message, ranked in crescent order of driver-agency relevance: 1. Multi-sense HMIs. A few HMIs are used to reinforce the sensorial impact of the car, in particular the dynamics of light and scent design, which change according to the selected driving mode. For instance, if AD is selected, the audio HMI is very bright, consonant, with smooth attack and release shapes, and a soft diffusion level suggesting low tension, calm, and focusing on bright, sky-like lights. These HMIs have long durations (5 to 10 sec.), mimicking the slow evolution of lights. 2. Low driver-agency relevance HMIs. Some HMIs simply aims at informing driver/passengers about decisions made by the central processing system. We call them "brain sounds". They are used to reinforce and reassure passengers that the autonomous driving operates without flaws. The ones called Line change and Trajectory change inform that the car makes a change of direction, before the centrifugal force shift is felt. The one called Everything OK is a periodic reminder that everything is working well and under control. For these sounds we used a low-intrusiveness/annoyance paradigm (Sec. 3.3). We created short and high pitched sounds (fundamental between 5 and 8 kHz.) which can be diffused at low amplitude in the cockpit and still be clearly perceived, without being aggressive. 3. High driver-agency relevance HMIs. Other HMIs are much more important from the driver-car relationships point of view, as they signify the need for a specific action. In that case, we decided to shape them by using the concept of "earcons" (12): musical signals with specific meanings, that usually imply a learning process. Their fundamentals' pitch-range are lower (200 to 500 Hz), and their duration are longer (main sections of 1000/1500 ms., followed by a reverb fade out).

For AD switches, we used the bichord [G B] to signify availability, and the bichord [Bb D] to signify locking: i) with the presence of a brain sound and a slow relaxing fade-out to signify that the system is locked in AD mode (the brain sound signifies the on-going process to AD mode; the slow fade-out suggests the possibility to keep hands off the steering wheel … and relax) ; ii) without the presence of a brain sound and with a faster, more dynamic, fade out to signify that the system is locked back in manual driving mode. We used a dissonant mixture of the 2 former bichords [G Bb B D] to signify that the AD mode needs to be abandoned, to go back to the manual driving mode.

For the Minimum Risk Maneuver situation, we used a single note [A], that is different from the previous ones: the car is going to stop, because the AD process has failed and the driver didn't take over. This is a rather serious situation which has two levels of severity. Therefore, we designed two version of this specific HMI, with two different levels of perceived urgency: the first one is a loop of 2500 ms., with a smooth timbre, meant to warn passengers without panic; in contrast, the second one has a pulsation of 500 ms. and a timbre characterised by a high roughness ratio -in other words, a classic alarm sound, announcing the danger of a possible impact.

Figure 3 shows the sonogram of the sound AD Definitive. It begins with a fast succession of two brain sounds, to remind passengers that the car is in Autonomous Drive mode. The next section signifies the locking onto AD mode, with a smooth fade-in and a slower fade-out, meant to suggest that the pilot can take hands off the steering wheel. Finally, the end of this HMI is a cross-fade with a longer and brighter sound synchronized to the change of the light system's brightness.

ZOOM OUT OF THE PROJECT

What We Learned?

The extreme density and complexity of the Symbioz project, added to its multidisciplinary aspect, makes it relevant to include it in a research-through-design approach (8), and then consider it as a project-grounded research that aims at "producing knowledge instead of only solutions" [START_REF] Findeli | La recherche-projet en design et la question de la question de recherche: essai de clarification conceptuelle[END_REF].

Globally, the Symbioz experience brought lessons in three main areas. First, multisensory design that led the sound design process to be inspired of, and composed with, other design dimensions. This involved fruitful collaborative design sessions conducted by designers (the Superface team) that resulted in transforming formalized design brief and insights to integrated design elements either in visual, auditory or olfactory domain. This forced to think about sound no more by itself but rather in interaction with other perceptual or experiential dimensions. Second, the intrinsic inclusion of technology into the sound design process that, at some points, appeared to be more densifying than lightening the integration of sonic components in the whole project. Actually, this densification mainly came from the fact that Symbioz was a high-level technological project and that its priority target, in terms of technology, was naturally the autonomous driving function. This assumption consequently addressed issues about technological independence of the sound design discipline -for how much should sound be dependent from its technical environment? -, but also about technological sobriety that leads, in a way, to reconsider the Bauhaus approach -and especially Mies Van der Rohe's maxim "less is more" -in the light of sound design. Third, the crucial needs of tools for prototyping and composing sounds. Here, the multiplicity of the application (60 sonic HMIs in all) led sound designer to develop generic tools to methodically build sound matters and morphologies (sketcher) and organize/arrange them in a pitch-rhythm-timbre parametrical space (meta-score). This pragmatic paradigm -that appeared to be efficient either for production but also for collaboration -could be related to the contemporary music composer practice when he/she works on the electronic components of his/her piece, gathering sound materials (record, synthesis, etc.) then making the arrangement with other sound sources (instruments, voices, etc.) in a global score.

Moreover, the main lesson that could be learned from the particular context of Autonomous Drive application is that sounds could definitely play a leading role in (future) daily life situations where other sensory modalities are obstructed or already recruited. This assumption has been objectively demonstrated by several previous scientific works in many different domains such as aircraft cockpit [START_REF] Kazem | Design considerations for a background auditory display to aid pilot situation awareness[END_REF], control room [START_REF] Cabrera | Development of auditory alerts for air traffic control consoles[END_REF] or surgical operating unit [START_REF] Patterson | Alarm sounds for medical equipment in intensive care areas and operating theatres[END_REF]. In the automotive frame, among many others, Tardieu et al. showed that sonifying the navigation process in a cockpit central unit (processing GPS, listening to music, searching contacts, … in the on-board computer) significantly increase the time spent to look at the road -the driving attention -, and decrease gaze movements from the windscreen to the computer screen [START_REF] Tardieu | Sonification of in-vehicle interface reduces gaze movements under dual-task condition[END_REF]. In the Symbioz Autonomous Drive configuration, driver/passengers' sight is theoretically used for any other things than looking at the road or at the dashboard. Theneven if it has not yet been objectified as such -it seems that sounds could be of a great help to make driver and passengers better informed of what currently happens, more confident in the technology and finally more safe, for instance, by avoiding wrong interpretations and unexpected actions.

Contribution to Sciences of Sound Design

The last elements of reflexion (Sec. 3.1) precisely comes into the conceptual frame we recently tried to make emerge and promote about sound design, in a tentative of integration of this field into the broader scope of design research. In fact, by especially transposing Nigel Cross' vision of "science of design" and his formalization of three design research sources (loci) -"people, processes and products" - [START_REF] Cross | Designerly ways of knowing: Design discipline versus design science[END_REF][START_REF] Cross | Designerly ways of knowing[END_REF], we look at laying the foundations of sciences of sound design that will respectively investigate the character of the sound designer, the sound design processes, tools or methods and the status of the designed sound, i.e. what sound design finally produces [START_REF] Misdariis | Sciences du Design Sonore -Approche intégrée du design sonore au sein de la recherche en design[END_REF]. Then, to some degree, we can expect that the present project may help to contribute to this approach and inform it. For instance, the two tools built on purpose (rapid sketcher and meta-score) or the conceptual thought on low-intrusiveness developed at the beginning of the Symbioz sound design process -and partially inspired by interactions with other design fields -bring links for a global knowledge on sound design, here understandably considered as "a discipline of study in its own right" [START_REF] Archer | Design as a discipline[END_REF] (see precisely Sec. 3.3 below for a development on low-intrusiveness). In the end, and quoting Cross [START_REF] Cross | Designerly ways of knowing: Design discipline versus design science[END_REF], this may give some elements for answering the seminal -and open -question: is there a designerly way of knowing, thinking and acting in sound design?

Immediate Intrusiveness vs. Long-term Annoyance

Indeed, the Symbioz demo-car project has been a fruitful laboratory to explore and refine a series of ideas and guidelines about low-intrusiveness and low-annoyance sound design. Actually, we started developing these guidelines since years, during our long-haul collaboration with Renault. During many projects, and thanks to reports from testers and clients, we had the opportunity to study how sounds played inside a car cockpit might be immediately perceived as intrusive (at first listening), or could become annoying in the long term (after repeated listenings).

On this basis, and with the support of literature [START_REF] Preis | Intrusive sounds[END_REF][START_REF] Fidell | Scaling the annoyance of intrusive sounds[END_REF], we started to draw few principles about designing sounds with a minimum of intrusiveness and annoyance impact on the listener. These pragmatic hypothesis are as follows: (a) foreground/background: intrusiveness of a sound is related to its background; (b) timbre: a few timbral features are directly linked to intrusiveness; (c) duration: intrusiveness of a sound is linked to its duration; (d) memory: annoyance can be enhanced by the presence of easy-to-remember pitched elements and rhythmic patterns; (e) context: the user context in which sounds are heard plays a role in determining if and how people judge them as annoying.

But, there are three inherent difficulties in considering these guidelines. First, they constitute a system of constraints by themselves, with their ingrained complexity. Second, they might be in direct contrast with standard principles of sound design. For instance, in a complex human-machine interaction (e.g., airplane cockpit) a certain type of sounds, such as important alerts, are processed as earcons (12) that needs to be learned and stored in memory; this feature being in contradiction with point (d) above. So, this means that, sometimes, low-intrusiveness and low-annoyance are features which can't be assigned to particular sounds. The third difficulty is that in a sound design project, solutions to point (a) and (e) are not exclusively in the hands of sound designers. For instance, the quality of the background sound in a car cockpit depends on how much the car manufacturer decides to invest in isolation and sound-proofing. In conclusion, low-intrusiveness and low-annoyance can be obtained only if all the stakeholders of a project agree on their importance. This being said, the sounds played in Symbioz's cockpit were tentatively designed by following the guidelines described above. Low driver-agency relevance sounds, destined to be passively heard many times during a trip (e.g., brain sounds -see Sec. 2.4), tried to be made as less intrusive and less annoying as possible. Actually, they were produced, mixed and diffused in order to minimize their emergence from the cockpit's background noise, while maintaining a minimal saliency to ensure their audibility. On the other hand, important auditory alarms, destined to be heard in very exceptional cases, were designed to clearly emerge from the background, then paying little attention to point (d).

CONCLUSIONS AND PERSPECTIVES

The Symbioz demo-car project has focused on an electric and autonomous vehicle and the ways of implementing unprecedented user experiences in the automotive domain -or broadly the near future of the mobility. One of these experiences is supported by the Autonomous Drive (AD) mode that Symbioz has implemented in the fourth (mind off) standardized level. This driving mode allows driver/passengers to do something else than driving, and even looking at road or dashboard, and subsequently allows sound to play an important role in the human-machine communicaiton process. In that frame, sonic Human-Machine Interfaces (HMI) were designed, produced and implemented in Symbioz cockpit, that relied on the highly technological software and hardware environment that the demo-car provided, especially considering the embedded multi-channel sound diffusion system which led to define the concept of spatial sonic HMI. This complex and multi-disciplinary project also allowed us to develop thoughts (the concept of low-intrusiveness) and tools or methods (rapid sketcher and meta-score) which can contribute at the same time to formalise the sound design process, to increase generic knowledge on this discipline and to draw the conceptual framework of sciences of sound design that we progressively tend to promote.

The perspectives of such a project could be plentiful, either in the short, medium or long term. In the short term, the crucial question of evaluation of the designed sounds (especially in the AD mode) naturally raises and could be potentially considered in a more analytical approach using, for instance, a car simulator or a virtual reality protocol to test the relevance of the produced artifacts together with their underlying hypothesis. In the medium term, the question of transposing the current sound design process to an industrialised frame (e.g., a mass production) could be of a great importance, as the present approach still implemented prototypic approaches (according to development workflow, tuning methods, etc.) that would be hardly feasible in an industrial context. In the long term, the question on how this singular experience in the automotive world could impact a general reflexion on the use of sound for interactions between human and machines could be opened and, if need be, transposed to many other research and application fields, among which the medical or spatial ones.

Figure 1 -

 1 Figure 1 -(1a, left) Works on the Symbioz mock-up by different teams (software, sound, light, etc.) at Renault's studio; (1b, right) Works on the Symbioz final version in the very late of the project.

Figure 2 -

 2 Figure 2 -Depiction of the spatialisation concepts of the three driving modes of Symbioz: natural for Classic (left), focused for Tonic (middle), diffused for Autonomous (right)

Figure 3 -

 3 Figure 3 -Sonogram of AD Definitive HMI (from left to right red circles ): brain sounds reminding AD mode setting; sound confirming successful locking in AD mode; multisense sound reinforcing light design.

https://www.devialet.com/fr-fr/story-symbioz-demo-car/

https://www.superface.fr/

ACKNOWLEDGEMENTS

The authors are thankful to Prof. Ercan Altinsoy, Prof. Katsuya Yamauchi and Prof. Masayuki Takada for their invitation to participate in the Structured Session on "Design of warning sound: from honking impact to sound quality in electric vehicles" of the ICA 2019 conference.

The authors are also thankful to Nathalie Gaittet (Renault Innovation Lab), Bertrand Fatus and Erwan Gorge (Devialet), Elise Hauville, Charleyne Serreau and Marion Chabod (Superface), Janick Deneux (ESAD TALM Le Mans) for their tight and fruitfull interactions in the Symbioz project.