Développement d'un critère synthèse de confort acoustique relatif au bruit de roulement dans une voiture particulière

UNIVERSITE DE

SHERBROOKE

Faculte de genie

Departement de genie mecanique

DEVELOPPEMENT D'UN CRITERE SYNTHESE DE CONFORT ACOUSTIQUE

RELATIF AU BRUIT DE ROULEMENT DANS UNE VOITURE PARTICULIERE

Memoire de maitrise es sciences appliquees

Specialite: genie mecanique

Composition du Jury:

Prof. Alain Berry (Directeur}

Prof. Patrice Masson (Directeur)

Dr. Colin Novak

Prof. Stephane Moreau

Francois BERGERON

Sherbrooke (Quebec], Canada Octobre 2009

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RESUME

Au terme de cette etude, un critere objectif synthetisant la perception du confort acoustique sera defini pour le cas du bruit de roulement dans une voiture particuliere. Le bruit genere lors du roulage n'etant actuellement defini que par son niveau acoustique global, on veut expliquer sa perception par rapport a des calculs psychoacoustiques valides. Pour ce faire, l'analyse sensorielle est utilisee afin de decrire le bruit de roulement par des descripteurs sensoriels. D'abord, a partir d'une banque de sons preenregistres en conditions controlees, une ecoute en salle sur jury permet de dresser la liste des termes les plus evocateurs pour decrire les aspects perceptifs du bruit de roulement. Ensuite, chaque membre du jury evalue l'intensite de chaque aspect retenu sur une echelle d'intensite de 0 a 15. Parallelement, une serie de calculs effectues par traitement du signal attribuent des valeurs psychoacoustiques sur ces memes enregistrements. Une etude statistique de regression multiple permet alors de lier les intensites percues a des metriques psychoacoustiques, pour rendre objectif le caractere subjectif de la perception acoustique. Les resultats attendus sont sous forme d'une liste de descripteurs perceptifs, chacun ayant ete correle avec une fonction ou un algorithme applique a un enregistrement binaural. L'integration de ces fonctions dans un logiciel synthese d'analyse permet d'anticiper l'appreciation client. Ce travail s'insere dans le cadre plus general d'un deploiement oriente vers les attentes de differentes typologies de clients chez Renault S.A.

Pour des raisons de confidentialite, les descripteurs de meme que les coefficients de regression liant les descripteurs aux metriques psychoacoustiques ne sont pas explicites. Par ailleurs, ce memoire se concentre sur la methodologie adoptee ainsi que sur l'interpretation des resultats.

REMERCIEMENTS

Ce projet de maitrise n'aurait pu avoir lieu sans Renault S.A., qui a mis a sa disposition de considerables ressources humaines, financieres et materielles. L'aide financiere d'AUT021, le reseau canadien de centres d'excellence en recherche et developpement sur l'automobile, est aussi soulignee.

A travers ces quelques lignes, je tiens a remercier Alain Berry, qui m'a invite a accomplir un premier stage au GAUS a l'ete 2003. Grace a lui, cette experience memorable me menera au volant d'une Renault Twingo, en Normandie, quatre annees plus tard... Qui l'eut cru?

Un grand merci a Patrice Masson, qui par son engouement soutenu, m'a encourage et appris la rigueur scientifique ecrite et verbale, meme a travers 10 heures de decalage horaire. Sans son appui, la redaction et la presentation scientifiques me seraient certes moins familieres.

Je me dois de souligner les memorables cooperations academiques a la maitrise avec Etienne Poulin et Philippe Brazeau; ces reussites constituent pour moi des episodes de travail en equipe absolument exemplaires.

Et que dire des Sylvain, Laurent, Terry, Yannick, Serge, Antoine, Francis, Landry, Emmanuel et de tous ces collegues du service acoustique Renault 64866... Votre sens de l'accueil a transforme un simple stage international en une experience de vie tout a fait indelebile. Jamais n'oublierai-je les sourires partages en votre chaleureuse compagnie.

Je tiens a mentionner l'aide de Celine Astruc, ingenieure en analyse sensorielle chez Renault, qui m'a epaule a travers l'apprentissage d'une science complexe, celle des sens humains. Son professionnalisme et son enthousiasme au travail ont fait de notre cooperation un reel plaisir. Clin d'oeil a la mysterieuse Jurate, sans qui les subtils accents baltes me seraient malheureusement inconnus.

TABLE DES MATIERES

CHAPITRE 1 : INTRODUCTION 1

1.1 Etat des connaissances 2

1.2 Objectifs 6 1.3. Methodologie 6

1.3.1 Campagne de mesures acoustiques et elaboration d'un profil sensoriel 7

1.3.2 Etude numerique et correlations inherentes 7 1.3.3 Etablissement d'un standard devaluation 8

1.4 Contribution scientifique 8

CHAPITRE 2 : SOUND QUALITY ASSESSMENT 9

2.1 Abstract : 12

2.2 Introduction 12 2.3 Sensory analysis protocol 15

2.3.1 Measurement set-up 16 2.3.2 Generation of descriptive terms 17

2.3.3 Training in magnitude rating , 19

2.3.4 Final evaluation 19 2.4 Correlation with objective metrics 20

2.5 Results of the sensory analysis 23 2.5.1 Generated lexicon and performance of the panel 23

2.5.2 Sensorial positioning of sounds ; 26

2.6 Results of the regression analysis 27

2.7 Conclusion 29 2.8 Acknowledgments 29

CONCLUSION 30 BIBLIOGRAPHIE ...32

LISTE DES FIGURES

Figure 1.1 Coefficient de correlation entre le niveau de bruit exterieur et le niveau textural 3

Figure 1.2 Schema du projet 6 Figure 2.1 Example of a linear scale used by the listeners 19

Figure 2.2 Illustration of the reliability, correctness and accuracy of a measuring device 20

Figure 2.3 Results from the Principal Component Analysis 27 Figure 2.4 Example of the regression analysis on dimension D4 28

LISTE DES TABLEAUX

Tableau 1.1 Parametres texturaux ayant un effet sur le bruit de roulement 2 Table 2.1 The four driving conditions are combinations of road surface textures 16

Table 2.2 List of the twelve listening sessions 18 Table 2.3 Repeatability of the five listeners' evaluations 24

Table 2.4 Results of the one-way product ANOVA on the listeners' evaluations 25 Table 2.5 Coefficients of determination associated to the 10 proposed predictive calculations ...28

LEXIQUE

Avant d'aborder le sujet principal de ce memoire, il est necessaire de clarifier le sens de certains mots ou expressions utilises a travers celui-ci. Une attention particuliere a ete portee sur l'usage du vocabulaire encadrant l'analyse sensorielle et la mesure. Les concepts suivants sont precises:

Voiture particuliere : Vehicule affecte en propre a quelqu'un; voiture privee.

Mesure objective : Mesure realisee dans un but de repetabilite et de repetitivite. Elle s'applique

pour une mesure instrumental, l'instrument pouvant etre un humain.

Mesure subjective : Mesure qui releve du sujet individuel. Comprend a la fois la perception et

l'interpretation. La perception est liee au traitement global du stimulus tandis que l'interpretation est liee a son evocation. Dans certains cas, si on s'attend a une repetabilite, une mesure peut etre a la fois subjective et objective. Pour eviter cette ambigui'te, on voudra eviter ces termes.

Jugement hedonique: Exprime la preference personnelle de sujets, possiblement influences

par la culture, le vecu...

Descripteur (sensoriel) : Terme decrivant un element de la perception du produit analyse. Dimension perceptive : Echelle permettant de quantifier l'intensite de la perception liee a un

descripteur.

Metrique ou indicateur psychoacoustique : Modele numerique representant l'intensite d'un

phenomene perceptif relatif a l'acoustique (p. ex. calcul de la rugosite percue d'un son).

Roulage : Action de rouler, contrairement a « roulement» qui se rapporte a son etat. (p. ex. une

seance de roulage sur piste pour ecouter le bruit de roulement).

Espace-produit: Ensemble incluant la totalite des produits vises par l'etude sensorielle. On

voudra selectionner une echantillon de produits representatif de cet ensemble, (p. ex. l'espace-produit des bruits de klaxon pourrait etre represente par une selection de sons de chaque

CHAPITRE1

De plus en plus, la clientele automobile considere le confort acoustique comme un critere important dans la selection d'un vehicule. En effet, la presence d'un haut niveau de bruit peut occasionner diverses consequences indesirables incluant la fatigue du conducteur et la difficulte des appareils de communication a discerner la voix du bruit present dans l'habitacle. De decennie en decennie, les constructeurs ont done ete amenes a reduire le niveau de bruit global des le stade de la conception. Le bruit du groupe moteur-ventilateur et du systeme de propulsion ayant ete controle par differentes approches, celui cause par le contact pneu-chaussee est devenu une source preponderate d'emission sonore des vehicules d'aujourd'hui pour des vitesses usuelles [1]. D'autre part, la tendance vers l'allegement des vehicules constitue une contrainte face aux solutions acoustiques traditionnelles, faisant usage de materiaux absorbants ou amortissants.

Afin d'accentuer l'effet des solutions a privilegier pour reduire le bruit de roulement, il importe d'examiner son impact sur l'humain. Plutot que d'en reduire le niveau sonore global, on voudra optimiser les aspects qui influencent davantage la perception humaine. Cette demarche constitue la premiere etape vers une conception axee sur la qualite sonore, qui peut se definir comme le degre auquel la totalite des attentes sur un evenement auditif sont rencontrees. Deux groupes divisent ces evenements: les bruits nuisibles et l'information de fonctionnement. C'est en etudiant la qualite acoustique qu'on pourra reduire le premier et faconner le second. II est connu que trois facteurs decrivent cet ensemble influencant la qualite acoustique: la physique (environnement acoustique), la psychoacoustique (perception auditive) et le psychologique (jugement).

Cette etude vise principalement a lier les facteurs physiques et psychoacoustiques afin d'obtenir une base devaluation qui pourra eventuellement ameliorer la satisfaction du client.

1.1 Etat des connaissances

Certaines recherches se sont penchees sur le bruit de roulement d'un point de vue purement physique: on cherche a decrire les liens entre ce type de bruit complexe et ses particularit.es

spectrales [1] [2] [3] [4]. II est d'abord reconnu que le bruit de roulement est substantiellement dependant du revetement routier. La plupart des phenomenes generateurs sont controles par les caracteristiques de la chaussee (par generation, amplification ou reduction sonore). Ce principal element excitateur du pneu a fait l'objet de plusieurs etudes relatives a la reduction de bruit. Bien que tous les liens entre les mecanismes generateurs et la texture du revetement n'aient pas encore ete traces, il est connu que les parametres suivants ont une influence plus ou moins marquee sur le niveau de bruit genere :

Tableau 1.1 Parametres texturaux ayant un effet sur le bruit de roulement [3]. Parametre Megatexture Macrotexture Microtexture Mauvais uni Porosite Epaisseur Adhesion (normale) Friction (tangent.) Raideur Degre d'influence Eleve Tres eleve Faible-modere Faible Tres eleve Eleve pour surfaces drainantes Faible-modere Faible-modere Incertain

Des correlations pertinentes ont ete trouvees en comparant le spectre textural d'un revetement avec le spectre du bruit genere a l'exterieur du vehicule. En conditions de roulage, des niveaux de pression sonore ont ete mesures pour 25 bandes en tiers d'octave, centrees en frequences/ Les niveaux d'amplitude texturale, resultats de la transformee de Fourier de la trace du profil routier, sont mesures aussi en 25 bandes en tiers d'octave, centrees en longueurs d'ondes X. Pour une frequence / et une longueur d'onde A definies, il existe n paires de niveaux de pression sonore (SPL) et de niveaux d'amplitude texturale (TL), n representant le nombre de revetements

a l'essai (entre 16 et 33). Une analyse par regression lineaire (sur le nuage de n points (SPL.TL) ) permet alors de quantifier la correlation entre la texture et le bruit pour ce couple (f, A). Le coefficient de correlation est defini comme suit, pour des echantillons:

rSPL,TL

-in - l)sSPLsTL

ou SPL , TL, sSPL et sTL represented respectivement les valeurs moyennes et les ecart-types de

niveaux de pression et de niveaux d'amplitude texturale sur les n points. En balayant les 625 combinaisons de frequence et de longueur d'onde, on peut tracer la cartographie complete, revelant les tendances generates a travers les bandes en tiers d'octave. Une correlation fortement positive a [f, A) implique qu'une augmentation de l'amplitude d'une composante A du revgtement routier amene systematiquement une elevation du niveau sonore a la frequence/. En contrepartie, une correlation fortement negative implique qu'une augmentation de l'amplitude de la composante texturale A amene une diminution du niveau sonore en /. Void un graphique presentant ces coefficients pour quatre types de pneus :

315 100 10 X ( m m ) 3 2 10 X (mm) 3 2

TIRE W «;•£»». itK r/ur.

10 \ (mm)3.2 10 \ ( m m ) 3 2

Figure 1.1 Lignes de contour du coefficient de correlation entre le niveau de bruit exterieur et le niveau de texture (vitesse de 80 km/h) pour 4 pneus. Les nombres encercles denotent les valeurs extremes aux frequences et longueurs d'ondes de profil« critiques ». Sculptures de pneu: X=ete, W=hiver, S=ete, P=lisse

Cinq observations sont notees:

• L'allure generate est similaire pour les quatre types de pneus

• La correlation est a la fois positive (le bruit s'accentue avec la texture] et negative (le bruit diminue avec la texture) selon la longueur d'onde de texture

• La frequence a laquelle la correlation change de signe, appelee frequence de transition, se situe autour de 1000-1250 Hz

• La correlation est maximalement positive entre le bruit 400-500 Hz et la longueur d'onde texturale 63-80 mm {frequence critique basse)

• La correlation est maximalement negative entre le bruit 2500-8000 Hz et la longueur d'onde texturale 2-3.2 mm {frequence critique haute)

Une analyse subsequente, cette fois prouvant la faible correlation entre les niveaux de pression sonore aux deux frequences critiques, a mene a la conclusion que le bruit de roulement global est le resultat d'une superposition de deux phenomenes generes independamment. La meme etude demontre que le bruit basses-frequences suit la droite / = v/A ou v est la vitesse de deplacement du vehicule et A la longueur d'onde texturale. Cette constatation n'est pas reproduite en hautes frequences, ou le bruit rayonne semble constant a A = 2-3.2 mm, quelle que soit la vitesse du vehicule. II suit que le bruit basses-frequences serait produit par l'interaction de la sculpture sur la macrotexture, alors que d'autres phenomenes genereraient le bruit hautes-frequences.

Ces niveaux qualifient le bruit percu a l'exterieur du vehicule et proposent des liens pertinents avec ses mecanismes generateurs. Dans le cas du bruit interieur, l'excitation reste la meme mais celle-ci se voit filtree par les differents organes de la voiture.

Par ailleurs, d'autres etudes portent sur la modelisation de la transmission du bruit de roulement vers l'habitacle [5]. On propose trois mecanismes : transmission directe a travers des fuites sur les parois, transmission de type «loi de masse » par les parois et finalement, le rayonnement acoustique par celles-ci. Bien que des simulations numeriques apportent des resultats interessants sur ces mecanismes de generation et de transmission, le lien avec la perception humaine reste a etablir.

Mis a part les etudes confidentielles menees chez les constructeurs automobiles, on trouve quelques publications qui mettent a profit les outils de la qualite sonore pour l'industrie automobile. Plusieurs protocoles ont mene a des resultats probants quant a son application sur des bruits simples (demarreur, echappement) ou complexes (impression deplaisante du bruit moteur) [6] [7] [8]. Dans la grande majorite des cas ou Ton fait mention de la qualite acoustique automobile, on cherche le lien direct entre un indicateur (physique et psychoacoustique) et un jugement (appreciation globale), sans se soucier d'enumerer d'abord toutes les dimensions perceptives qui risquent d'influencer ce jugement. Par exemple, on compare deux sons et on demande au sujet de specifier lequel il prefere (methode de comparaison par paire) [9]. On postulera alors que les facteurs physiques les plus influents sont ceux qui sont le plus correles au jugement tire d'ecoutes par un jury compose de non-experts. Ceci est vrai dans la mesure ou les produits testes couvrent bien l'espace-produit et ou seule 1'appreciation globale est souhaitee. Par contre, 1'appreciation parametrique, plutot que globale, est plus informative pour repondre a des problematiques precises (plaintes client ciblees dans un pays, une culture, sur une chaussee donnee...).

Hormis les etudes hedoniques mentionnees ci-haut, certaines s'adressent a des aspects bien definis de l'acoustique automobile, par exemple le grondement moteur («rumble»), le graillonnement («rattle») de la boite de transmission et le caractere sportif du bruit d'echappement [10] [11] [7]. Dans tous ces cas, le jury connait d'avance l'aspect sonore a evaluer; les parametres sont etablis avant merae de proceder aux reecoutes. Cependant, la nature complexe du bruit de roulement rend difficile un parametrage aussi intuitif. C'est en outre pourquoi il vaut mieux, pour l'etude de ce bruit, insister avec rigueur sur l'enumeration detaillee des dimensions perceptives avant de lier des indicateurs a un jugement client.

Devant cet etat des connaissances, ce projet propose de definir une base constitute des dimensions perceptives relatives au bruit de roulement. A partir de celle-ci, une etude subsequente pourra lier ces differentes echelles au jugement hedonique du client.

1.2 Objectifs

L'objectif principal est d'acquerir une meilleure connaissance des aspects perceptifs du bruit de roulement. Ce dernier se decline en trois objectifs specifiques :

1. Recenser les descripteurs acoustiques pertinents a la problematique

2. Lier un calcul numerique, base sur les mesures physiques du'son dans l'habitacle, a chacune de ces dimensions perceptives associees

3. Proposer une methode devaluation de la qualite sonore relative au bruit de roulement sur des vehicules particuliers

Ces buts sont illustres dans la Figure 1.2 : les enregistrements sonores menent a l'enumeration des dimensions perceptives et des resultats de calculs num£riques. Les liens entre ceux-ci servent alors a rendre objectif l'aspect subjectif du bruit de roulement (« objectivation »), de sorte qu'ils peuvent etre utilises pour evaluer sa qualite sonore.

Bruit de roulement

Traitement du signal

Perception

Objectivation

Figure 1.2 Schema du projet

1.3 M e t h o d o l o g i e

Ces objectifs constituent une question qui devait etre repondue en douze mois, lors d'un stage au Centre Technique d'Aubevoye, chez Renault S.A. Plusieurs ingenieurs et techniciens de meme que les infrastructures du centre d'essai sur piste etaient a la disposition du projet. La methodologie suit les objectifs et en decoule directement.

1.3.1 Campagne de mesures acoustiques et elaboration d'un profil sensoriel

Les normes internationales ISO preconisent la caracterisation des revetements principalement par rapport a la mega-, macro- et micro-texture ainsi qu'au mauvais uni de surface [12]. Afin de batir une base sonore complete, on doit produire des enregistrements qui representent bien l'espace-produit. En s'inspirant de ces references, on fait varier les parametres influencant le bruit de roulement: le type de voiture, sa vitesse et le revetement routier. Pour ce faire, des vehicules particuliers de segment inferieur a superieur (sous-compacts a mini-fourgonnettes, excluant les vehicules utilitaires) sont selectionnes en choisissant diverses options (types de pneus, jantes en tole ou aluminium, presence de toit ouvrant, finition...} Ceux-ci sont conduits sur quatre chaussees visiblement differentes par leurs niveaux de macro- et de micro-texture. Une tete acoustique en position passager est utilisee pour realiser les enregistrements binauraux. Une fois cette banque de sons assemblee, on procede a un protocole d'analyse sensorielle: le profil sensoriel classique. Grace a ce dernier, on peut extraire les termes qui permettent a un individu de decrire et de differencier les sons. A partir de reecoutes en salle, un jury est appele a differencier les sons par rapport a des echelles basees sur des descripteurs communs. Ces mots doivent faire consensus a travers les membres du jury. Une fois la repetitivite confirmee, on etablit la liste finale des descripteurs formant la base perceptive relative au bruit de roulement. 1.3.2 Etude numerique et correlations inherentes

Une fois que les descripteurs influents sont decrits et enumeres, on amene le jury a evaluer la banque de sons suivant les dimensions perceptives associees. Les resultats de chaque dimension perceptive sont compiles comme des scores moyens (de 0/15 a 15/15) decrivant ainsi l'intensite percue par l'auditeur moyen. Ces evaluations doivent etre ensuite associees a un calcul qui permettra ensuite de predire la perception sans faire appel a un humain.

La regression lineaire multiple est l'outil choisi pour lier les dimensions perceptives a des metriques psychoacoustiques. Chaque dimension perceptive est analysee independamment. Pour chaque dimension, on doit d'abord « proposer » une serie de metriques a calculer sur tous les sons. Ensuite, l'analyse par regression optimise les coefficients ponderant chaque metrique propose, de sorte que l'erreur finale entre la prediction et la valeur mesuree est minimisee, la

valeur mesuree etant, dans ce cas, 1'evaluation moyenne decrite ci-haut. Generalement, les metriques possedent un bon pouvoir predictif si la correlation entre les valeurs predites et les valeurs mesurees est haute.

1.3.3 Etablissement d'un standard d'evaluation La finalite de cette etude prend deux formes :

• Une nouvelle grille d'evaluation dressee a partir des descripteurs pertinents;

• Un logiciel de, calcul predictif programme a partir des metriques psychoacoustiques correlees.

Tandis que la grille permet une evaluation perceptive in situ par un acousticien, le logiciel propose une analyse tiree d'enregistrements binauraux realises sur piste.

1.4 Contribution scientifique

La presente recherche propose un moyen de mieux cerner les composantes perceptives du bruit de roulement transmis au chassis en vue de les optimiser. Elle constituera d'ailleurs une nouvelle application de l'analyse sensorielle, encore rarement utilisee en acoustique. La meilleure comprehension de ce type de bruit permettra de cibler certains parametres influencant la qualite sonore dans l'habitacle et d'en evaluer l'impact sur l'individu.

Au terme de cette etude, l'equipe Bruits Systeme Chassis de Renault aura acquis un moyen d'evaluer les aspects influents de la qualite sonore attribute au bruit de roulement. Une fiche d'evaluation des vehicules servira a decrire avec precision les niveaux d'intensite des descripteurs perceptifs constituant le bagage acoustique. Un logiciel de traitement du signal permettra de calculer les valeurs numeriques associees d'apres les enregistrements. Ces outils orienteront le deploiement subsequent selon la notion de qualite acoustique.

A plus long terme, les livrables de cette etude pourront offrir une notion d'equilibre hedonique, a laquelle seraient associes indicateurs perceptifs et typologies client.

CHAPITRE 2

EVALUATION DE LA QUALITE SONORE DU BRUIT DE

ROULEMENT INTERNE AUTOMOBILE A L'AIDE DE LA SCIENCE

SENSORIELLE

SOUND QUALITY ASSESSMENT OF INTERNAL AUTOMOTIVE

ROAD NOISE USING SENSORY SCIENCE

AVANT-PROPOS

Auteurs et affiliations:

F. Bergeron : Etudiant a la Maitrise, Universite de Sherbrooke, Faculte de genie, Departement de genie mecanique.

C. Astruc : Ingenieure en analyse sensorielle, Renault - TCR AVA 1 65, 1 Avenue du Golf, 78288 Guyancourt Cedex France.

A. Berry: Professeur, Groupe d'Acoustique de l'Universite de Sherbrooke (GAUS), Universite de Sherbrooke, Faculte de Genie, Departement de genie mecanique.

P. Masson: Professeur, Groupe d'Acoustique de l'Universite de Sherbrooke [GAUS], Universite de Sherbrooke, Faculte de Genie, Departement de genie mecanique.

Date de soumission : 18 juin 2009

Revue : Acta Acustica United with Acustica

Titre francais : Evaluation de la qualite sonore du bruit de roulement interne automobile a

l'aide de la science sensorielle

Contribution au document: L'article constitue le corps de ce memoire et decrit en details la

methodologie ainsi que l'analyse des resultats decoulant de l'etude effectuee.

Resume francais : Cette publication presente une technique decrivant la perception du bruit de

roulement interne automobile. Tandis que les sources acoustiques automobiles atteignent des niveaux reduits, une attention particuliere est accordee au confort acoustique. Alors que la connaissance des mecanismes derriere la generation du bruit de roulement est croissante, sa perception par le conducteur demeure relativement inexploree. Dans cette etude, une technique de la science sensorielle - le profil sensoriel classique - est appliquee pour obtenir une description du bruit de roulement interne par des criteres perceptifs quantitatifs. Sept vehicules particuliers (de Renault, Fiat, Peugeot et Toyota) ont ete conduits sous quatre conditions de vitesse et de chaussee variees. Vingt-et-un enregistrements typiques de bruit de roulement ont

ete presentes a un jury de cinq experts acousticiens. Douze sessions de reecoute de trois heures, individuellement et en groupe, ont mene a une liste de 15 descripteurs perceptifs pertinents, comme par exemple la «douceur sonore». La performance des sujets a ete testee statistiquement: leur repetitivite, leur habilite a discriminer les sons et leur accord inter-sujet. Un accord suffisant a ete observe a travers les sujets pour 10 des 15 descripteurs, qui constitueraient la base perceptive du bruit de roulement interne automobile. A travers la regression lineaire multiple, de fortes correlations ont ete trouvees entre les dimensions perceptives et certaines proprietes psychoacoustiques des echantillons sonores. Les retombees de cette etude se resument a une grille d'evaluation pour assister les conducteurs d'essais et a un outil de prediction de la qualite sonore base sur les correlations entre les dimensions perceptives et les metriques sonores calculees.

2.1 Abstract

This paper presents a technique to describe the perception of internal automotive road noise. As the acoustic sources on a car reach quieter levels, special attention is granted to acoustical comfort. While the knowledge of mechanisms behind road noise is growing, its perception by the driver remains relatively unexplored. In this study, a sensory science technique - the classical sensory profile - is applied to obtain a description of the internal road noise by quantitative perceptual criteria. Seven consumer cars (from Renault, Fiat, Peugeot, and Toyota) were driven under four well-defined conditions varying in speed and road surface. Twenty-one typical road noise recordings were presented to a panel of five experts in acoustics. Twelve listening sessions of three hours, individually and in group, led to a list of 15 relevant perceptual descriptors, such as "sound softness". The performance of the listeners was tested statistically: their repeatability, their ability to discriminate the sounds and the inter-subject agreement. Sufficient agreement was observed across the listeners for 10 of the 15 descriptors, which would constitute the perceptual basis for internal automotive road noise. Through multiple linear regression, strong correlations were found between the associated perceptual dimensions and psychoacoustic properties of the sound samples. The findings from this study are summarized in a sensory grid to assist the test drivers in the evaluation of road noise sound character, and a predictive tool for sound quality evaluation based on correlations between perceptual dimensions and calculated sound metrics.

2.2 Introduction

Sound quality in the automotive industry has attracted considerable interest over the last decades. The impact of a car's acoustics whether from the engine, road or other components -is known to influence the perceived overall quality from a customer's point of view. Along with the advent of electric cars, internal noise due to engine and powertrain has attenuated drastically, accentuating the perception of other noise sources such as road-tyre interaction. It is now common knowledge that customers pay great attention to sound quality even when the sound is only a side effect of the product's operation [13] and that the overall interior sound pressure level is no longer sufficient to assess appreciation [14]. Noise sources must therefore be

identified and characterized independently from a perceptive point of view. The prediction and control of the perceived internal road noise is thus a crucial step for manufacturers in order to reach a marketing value which meets the customer's expectations.

Generation of external road noise has been explored by U. Sandberg [4] and divided into low and high frequency ranges. It has been proposed that the low-frequency portion of road noise is due to the impact of tyre thread elements on road chippings, or vice-versa. On the other hand, the high-frequency mechanism is believed to be some air-pumping or air-resonant mechanism perhaps excited by stick-slip motions in the tyre-road interface. To summarize the extreme complexity of the physics of road noise, Sandberg even states that the simplicity and apprehensiveness of presented relations are in proportion to either the limitations of data, or the author's ignorance [4]. Still in this paper, profilometry is suggested as a method to characterize road surfaces regarding the generated road noise. Typically, a laser-beam profilometer is used to record the road's longitudinal profile, whose spectral analysis leads to normalized "texture levels" in four wavelength (A) bands: microtexture (A < 0.5 mm), macrotexture (0.5 < A < 50 mm), megatexture (50 < A < 500 mm) and unevenness (A > 500 mm). Relationships linking spectral components of road noise to the profile texture have been established and are used as an ISO standard for the characterization of pavement texture regarding vehicle noise [12]. In another study, three mechanisms behind the generation of road noise inside the passenger cavity have been proposed by Lalor and Priebsch [5]: direct transmission from the outside through small holes in the cabin walls, 'mass law' transmission through cabin walls (excluding their resonant response) and acoustic radiation by the vibrating walls. These three mechanisms would explain the transmission of vibrational and acoustical energy (from the road excitation) to the passenger cavity. The same work presents numerical predictions of interior noise spectral levels that were achieved using finite element and boundary element method, statistical energy analysis, hybrid methods, ray tracing method, and band-averaged transfer function method. While generation and transmission of road noise have been thoroughly investigated, the question of human perception of internal road noise was not addressed as such.

Various methodologies for evaluating sound quality in the automotive industry were proposed by Miskiewicz and Letowski [13]. Two types of criteria for the assessment of sounds were distinguished: sound quality (degree of satisfaction) and sound character (quantitative

judgment). Sound character judgment does not imply anything about the listener's preferences or satisfactions, but relies on their perception of a certain aspect (e.g. roughness of an engine noise). Sound quality and character are to be evaluated by either global or parametric assessment. For example, parametric assessment of sound character makes it possible to examine the listeners' particular perception through a set of continuous scales called "perceptual dimensions", whose associated descriptive terms are called "descriptors". For the above-mentioned example, the descriptor would be "engine sound roughness" and its perceived magnitude would be evaluated by a listener, on a scale representing this specific perceptual dimension. In order to fully characterize the perception of a complex sound, one needs several perceptual dimensions, each of which representing the various "aspects" of the sound character. The object of the present study could therefore be defined as a sound character parametric assessment for internal automotive road noise.

Several studies have linked the satisfaction of a set of customers to psychoacoustical metrics computed on recorded sounds. Schiffbanker et al. [8] developed a methodology based on paired-comparison tests, principal component analysis and multiple regression stepwise analysis, applied to the prediction and reduction of the unpleasant impression in engine noise. Otto and Wakefield [6] studied the preference of automotive starter sounds using paired comparisons. Dedene et al. [7] also used paired comparison in addition to multiple linear regression to predict objectively the perception and appreciation of exhaust sounds. In these cases, the choice of the evaluation methods was justified by the type of listeners (not sound quality experts) and the goal of their work, assessing customer satisfaction through sound quality analysis.

Excluding the preference criterion, several studies addressed sound character in the automotive industry. Wakita et al. [10] proposed a one-parameter sound character assessment of the engine rumble noise using a panel of twelve listeners and synthesized engine sounds. Barthod et al. [11] studied the perception of noise known as "rattle" in gearboxes, using the dissemblance test technique. The authors compared a list of known significant factors to the variance portrayed by the parametric evaluation of the rattling sound aspect. Dedene et al. [7] also examined the correlation between sound perception of exhaust sound with sound quality metrics, again proposing an established set of parameters (sportiveness and pleasantness). Lee and Park [15] linked subjective evaluation of "booming" and "rumbling" perceptions to sound "indices"

(objective evaluations] using artificial neural network. Finally, Hashimoto [16] applied a correlation technique to describe the perception of booming and impulsion of engine noise in terms of usual psychoacoustical parameters. From this, it is clear that most of the studies related to sound character in automobiles propose in advance a known set of sound descriptors (e.g. rumble, rattle, sportiveness, boom...). The listeners are then asked to evaluate the magnitude of the corresponding perceptions using linear scales ("perceptual dimensions")- In the above cases, the underlying sound descriptors were precisely known to the listeners, such that they anticipate what to seek for. In the case of road noise, however, such descriptors are not widely known or accepted as universal. Primarily due to road noise's intricacy, these descriptors, whose relevance has never been confirmed, are usually noted subjectively and intuitively. This should explain why very little has been accomplished regarding sound quality or character of the perceived road noise: its complexity makes its evaluation challenging.

This study presents a complete approach to derive a list of descriptors for the characterization of internal road noise. Acoustical metrics are then correlated to the associated perceptual dimensions to propose a prediction of the perception as assessed by a human being. It should be noted that the presented methodology leads to a set of descriptors whose relevance has been thoroughly tested on a small number of sound experts. In this view, the results relate to this specific group and would require extensive validation if they were to be used by other evaluators or in another context (i.e. inducing cultural, experience, or language biases). Therefore, emphasis is brought to the methodology and analysis of results, which can be applied to a variety of contexts in order to assess the perception of automotive noise.

2.3 Sensory analysis protocol

Sensory analysis methods have been extensively developed in the mid-1900's by the food industry, and have also been applied to the cosmetics, textile, pharmaceutical and transportation fields. The classical sensory profile is one of the descriptive methods of sensory science which allows describing all of the sensorial properties of consumer products [17] [9] [18]. The description is based on the perception of a small group of trained listeners. The final goal of a classical sensory profile is to obtain an evaluation grid and a sensory positioning of the tested products in order to compare and describe them. In the case of this study, five acoustics experts

participated in twelve weekly listening sessions, each of which lasting approximately three hours. The choice of experts in acoustics (as opposed to non-expert customers) is motivated by their greater elocution regarding their acoustical perception and by the availability of such experts. Also, the final output being a tool for sound character evaluation, and not for customer appreciation assessment, it makes sense for a car manufacturer to choose technicians and engineers in acoustics as the listeners participating in the sensory profile.

2.3.1 Measurement set-up

A collection of sound samples was to be constructed from real-life situations where road noise prevailed. One critical step was to ensure that the presented sound samples were exhaustive and representative of the road noise usually heard by consumers. For this purpose, twenty-one different sounds were recorded in seven consumer vehicles (Renault Espace, Renault Modus, Renault Clio, Renault Megane, Fiat Panda, Peugeot 207, and Toyota Auris) according to four driving conditions. Following the recommendations from ISO standards [12], the driving conditions were specified in order to enhance road noise variations by combining two parameters known to influence road noise: pavement microtexture and macrotexture. As summarized in Table 2.1, four road surfaces (rough, protruding, smooth, highway) were associated with four driving speeds (60, 70, 80, 100 km/h), combining low and high levels of pavement micro- and macrotexture. The difference in speed between the driving conditions induced a variety of excitations caused by very large profile wavelengths (megatexture and unevenness). Through their different features (type of suspension, rim material, presence of a sunroof...), the seven vehicles were also selected in order to generate a variety of road noises.

Table 2.1 The four driving conditions are combinations of low and high levels of road surface textures.

Low microtexture High microtexture

Low macrotexture High macrotexture Smooth track, 80 km/h "Protruding" surface, 70 km/h

Highway, 100 km/h Rough track, 60 km/h

The sound pressure signals were sampled at 51.2 kHz and digitally recorded using the Head HMS binaural measurement system in the passenger seat. This equipment is of great importance as it

emulates the transmission characteristics comparable to human external hearing system [14]. Special care was taken to ensure that the road noise was predominant. Regarding engine noise, in all cases, the transmission was set to neutral. In addition, in highway conditions, where aerodynamic noise is an important contributor to interior noise, we ensured that no wind gusts (high-speed and unstationary flow) were included in the final recordings. From this sound bank, a limited number of 21 samples were selected for the listening tests. These were chosen for their variety and their signal cleanness. The sounds were then leveled to a common psychoacoustic loudness value [19], as this variable is known to be the main factor influencing sound perception

[8]. Eliminating loudness as a variable brings conclusions based on sound aspects rather than levels, which answers more adequately the underlying issue of sound character parametric assessment. The sessions were conducted in a room that allows simultaneous listening by the five experts, which facilitated the discussion between them. For each listener, a pair of high-quality STAX SR303 open headphones was made available for the medium and high frequency reproduction, the lower part of the spectrum being emitted by a Haeliatus HA-224 subwoofer. This ensured adequate reproduction over the entire frequency range. It should be specified that the listeners had visual access to a screen showing only 3-digit sound codes that were modified for every listening session.

2.3.2 Generation of descriptive terms

The classical sensory profile was conducted under three main stages, divided into 8 individual and 4 in-group listening sessions (shown in Table 2.2), the first stage being the generation of descriptive terms. The listeners were asked to individually write on a sheet of paper the lexicon they would use to describe and differentiate the sound samples that were presented through binaural playback. In order to accommodate the majority of listeners, each 5-second sample was looped three times, followed by a 10-second silence. This 25-second segment was repeated (6 times on average) until the listeners agreed that their individual list of descriptive terms was exhaustive for the presented sound. This process was repeated for the 21 road noise samples. Qualitative sorting was then accomplished from in-group listening and discussion (c.f. Table 2.2: session 2) and had for objective to eliminate hedonic terms (preferences), synonyms and antonyms. The following in-group discussion concerning the definition of the terms (session 3) allowed the listeners to agree on terms which first carried a different meaning among them.

Remaining non-obvious synonyms were identified and eliminated by the panel during this discussion. The reduced list of terms was used in sessions 4 and 5, where listeners were asked to evaluate the perceived magnitude of each descriptor, on each sound, on discrete 6-step scales ranging from 0 (no intensity) to 5 (strong intensity}. The objective of the subsequent quantitative sorting was to reduce this list by identifying the less used terms and the correlated terms [20]. Two statistical indicators were used for processing the quantitative data which consisted in the 0-5 scores from each listener, on every sound. First, the geometric mean, reflecting the usage of a descriptor, helped find terms that were used only for a minority of sound samples. Then, principal component analysis (PCA) was achieved in order to reveal high correlations between descriptors. This type of analysis allows visualization and interpretation of data by associating the greatest amount of observed variability to a reduced number of variables [18]. PCA permitted the quantitative validation of the assumption that all descriptors were used by every panelist and that strong oppositions or redundancies did not occur. Lastly, in sessions 6 and 7, two "anchor sounds" (low and high sound references) were chosen for each descriptor, to insure a consistent output on linear scales [21], eventually used in the next stage. At this point, a list of "potential candidates" for the descriptors of internal road noise was drawn. These descriptors were said to be relevant, precise, discriminating, independent, exhaustive and neutral (without preferences).

Table 2.2 List of the twelve listening sessions, grouped in three stages, constituting the protocol of the classical sensory profile. Each session took place weekly and lasted approximately three hours.

Session Sensory profile session content Type

Session 1 Session 2 Session 3 Session 4 Session 5 Session 6 Session 7 Session 8 Session 9 Session 10 Session 11 Session 12 Stage 1 : Generation of descriptive terms Generation of terms Qualitative sorting In-group discussion Quantitative sorting Quantitative sorting

Definitions of terms and anchors Validation of terms and anchors Stage 2: Training in magnitude rating

Stage 3: Final evaluation

Individual In-group In-group Individual Individual In-group In-group Individual Individual Individual Individual Individual Presentation of results

2.3.3 Training in magnitude rating

The second stage consisted in the training of the experts in magnitude rating. For each descriptor, the listeners evaluated the magnitude of the associated perception using linear continuous scales, marking the perceived magnitude of each sound (see Figure 2.1). For this purpose, a sheet containing the entire set of descriptor scales was provided to each listener. After the playback of each 25-second sound segment, the listeners were asked to draw a crossing line representing their perceived magnitude on every descriptor's associated scale. The score was measured as the distance in centimeters from the lower end of the 15-cm scale. The advantages of this magnitude estimation method are the absence of a "ceiling effect" and the theoretical infinite resolution [21]. The two sessions (Table 2.2, sessions 8-9) of this training phase brought the experts to use the total length of the scales, thus accentuating their discriminating power. This is the main objective of the training phase, as the actual results are not further processed.

Descriptor 1: Sound softness

-Mr- Mh

low sound 402 sound 759 high

Figure 2.1 Example of a linear scale used by the listeners to evaluate the magnitude of a perception between "low" and "high" limits. Actual scales used were longer (15 cm).

2.3.4 Final evaluation

The third stage of the classical sensory profile consisted in three sessions of magnitude rating. For each of these sessions, the experts were asked to evaluate the complete set of sounds for all of the sensory descriptors, again on linear continuous scales using the same low and high sound anchors. The two main objectives of this stage are to assess the panel's statistical performance using the relevant descriptors and to collect perceptual data on each sound.

In sensory analysis, since the experts are to be considered as measuring devices, they must be reliable, correct and accurate. As illustrated in Figure 2.2, reliability is the ability of a subject to produce, in specified conditions, similar responses to similar stimuli over time. Correctness

describes a subject for whom the average response is very close to the known, or expected value. An accurate subject possesses the characteristics of both reliability and correctness.

R e l i a b i l i ty Correctness Reliabm^anTco'rrectness

Figure 2.2 Illustration of the reliability, correctness and accuracy of a measuring device.

On relevant descriptor scales, the average of the individual measurements of a group determines the true or expected value of this measurement. As the responses usually vary slightly from a subject to another, it is necessary to collect the responses from several trained subjects. Consequently, three successive sessions of evaluation were accomplished to test the performance of the listeners. Their individual repeatability, their ability to discriminate the sounds and their mutual agreement were tested throughout the third stage of the sensory profile. Statistical techniques such as standard deviation, analysis of variance and principal component analysis, applied to the data collected in the final evaluation, led to conclusions regarding the performance of the panel and the relevance of the selected descriptors.

Furthermore, for sessions 8-12, the order of listening was randomized for every session using the latin square technique, thus minimizing the possible interaction between subsequent sounds [22]. At the end of this third and final stage of the sensory profile, the collected data can be used to assess the relevance of each descriptor and the accuracy of each listener.

2.4 Correlation with objective metrics

The following step aimed at correlating the panel's mean evaluated magnitude of each perceptual dimension (quantitative scale linked to a descriptor) to a combination of known objective sound metrics. The analysis should provide a set of metrics that is:

• Specific to each perceptual dimension.

• Representative of the average assessment of the experts and contained in the experts' standard deviation.

• Linked to perceptual phenomena documented in (psycho-)acoustics literature.

Additionally, it should be specified that the aim of the analysis was not to compare the validity of existing psychoacoustic quantities, nor to find the combination of objective metrics which provided the strongest correlation with the experts' mean evaluation, regardless of the number of implied variables. The proposed set of metrics should be as simple as possible in order to ease its interpretation and express a direct link between each perceptual dimension and its physical cause. The proposed calculations included the usual (psycho-)acoustic metrics [10] [19] [23] [24]:

• Octave, 1/2 octave, 1/3 octave and arbitrary band sound pressure RMS levels • A, B, C and D weighted sound pressure levels

• Zwicker loudness on 24 Bark bands

• Roughness and sharpness levels, fluctuation strength, modulation energy • Impulsiveness, tone-to-noise ratio (TNR)

• Spectral centroid, absolute deviations, kurtosis, skewness

In order to achieve correlations between the subjective perceptual dimensions and objective metrics, a multiple linear regression analysis was conducted. For a given perceptual dimension, we propose a set of n metrics computed on the 21 sounds. As each perceptual dimension is independent, separate analyses were led, such that the selection and number of calculations n varied among the perceptual dimensions. Not all of the above-mentioned metrics were included in every analysis; only those that primarily showed relevance to the analyzed descriptor. The values of each metric x is calculated on the 21 sounds, forming a form a matrix (xi,i...X2i,n) which, when multiplied by coefficient vector {a0 ... an}, yields predicted result vector {yx ...y2i}:

y2 ^ 2 1 , / = V vl,l v2,l v21,l .. X, _\,n V a ^ x 2,« X 2\,nJV*nJ a. a„ (2.1)

The results of the third stage (final evaluation) of the sensory profile were averaged between the listeners. These 21 quantities were considered to be the true or expected values {yx ...y2i) of the perceived magnitudes for this perceptual dimension. The optimal set of metric coefficients

{a0 ... an) minimizes the sum of the squared residuals calculated as a difference between the

prediction yt and the expected value yt:

Z Vi " Vi I = Z hi - (a0 + aXXi,\ + a2Xi,2 +- + anXi,n )f

i'=l j ' = l

(2.2)

For this purpose, a least-square algorithm was used to solve the above matrix system for optimal coefficients (a0... an). The quality of the regression was illustrated by the corresponding

coefficient of determination R2 computed between the predicted and expected values. The

outcome of this analysis is a set of coefficients that, matched with known metrics, yields predicted perceptual magnitudes with a high degree of statistical confidence.

Many of the proposed quantities required specific frequency limits to constrain the processing in particular spectral areas. For example, the sound pressure level in an arbitrary frequency band can be framed between a "lower" and a "higher" limit frequencies. On analyses where only one metric is proposed, these optimal values can be found by simple graphical optimization. By iterating the regression analysis and mapping the coefficients of determination (R2) as a function

of the two limit frequencies, one can visualize the optimal combination of lower and higher limit frequencies.

2.5 Results of the sensory analysis

2.5.1 Generated lexicon and performance of the panel

The objective of the first stage of the classical sensory profile was to generate a list of terms that would be used to describe and evaluate road noise. During the exhaustive generation of terms (Table 2.2, session 1), the five experts listed 183 terms describing the 21 sounds, varying from 17 to 60 terms by expert. The number of terms from two experts was significantly higher, as they used many analogies and synonyms to describe their perception. These 183 terms were then clustered, by semantic meaning, into 37 groups. From this clustering and the following in-group discussions, the experts reduced the number of results to 27 relevant descriptors which would be tested on 6-step discrete scales (sessions 4-5). From hypothesis testing, the main results of the quantitative sorting showed that, under a statistical confidence level of 95% :

• For 11 of the 27 terms, the geometric mean on the 0-5 scores was lower than 50 %, revealing either an infrequent use of the term by the listeners or a low occurrence of the corresponding perceptions in the sound bank. This did not automatically lead to eliminating these descriptors, since the performance of the panel was to be increased through training.

• 20 terms allowed differentiating sounds in a statistically significant way, such that seven were eliminated.

• Two terms were obvious antonyms according to the definitions of the experts and to the significant negative correlation shown by PCA. Two other terms were found to be positively correlated. Therefore two terms were hereby eliminated.

Three of the 18 remaining descriptors were eliminated through the following in-group discussions (sessions 6 and 7), because no consensus could be achieved regarding common definitions and (high or low) anchor sounds. Following the training in magnitude rating (sessions 8-9), results from the three sessions of final evaluation were collected and processed to measure the repeatability, discriminating power and agreement of the group.

2.5.1.1 Individual repeatability

In sensory analysis, the role of an evaluator is to estimate sound character in a most reliable way, such that the measured response does not change with time. This dispersion between responses is illustrated by the standard deviation computed on subsequent assessments. As shown in Table 2.3, the standard deviations between the three responses of each listener were significantly low. On average, expert E4 was the most reliable, followed by El, E2 and E5, and lastly, E3. In the third evaluation session, the listeners were presented 22 sounds, one of which being presented twice. The experts were not aware of this repetition; they were merely informed that a new sound had been added. A Pearson's correlation test [25] (hypothesis testing based on correlation coefficients] within the 15 evaluations on the two identical sounds and for each expert showed that the correlations were significant, attesting intra-session repeatability. In conclusion, except for expert E3, the listeners were thus generally reliable during the same session and throughout the procedure (intra-session and inter-session repeatability).

Table 2.3 Repeatability of the five listeners' evaluations as a measurement of their individual reliability. The numbers represent the average standard deviation between the three final evaluations computed over the set of sounds. Scores are recorded in centimeters on continuous 15-cm scales. In white, standard deviation a < 1.5

is considered acceptable; in dark grey a > 3 is considered unacceptable and in light grey, 1.5<o<3. Sensory descriptors

Listener Average D l D2 D3 D4 D5 D6 D7 D8 D9 DIP D l l D12 D13 D14 D15 Expert 1 Expert 2 Expert 3 Expert 4 Expert 5 Average 1.7 1.7 2.1 1.4 1.7 1.8 2.1 1.3 2.4 1.7 2.6 2.0 2.0 1.9 1.6 1.3 1.1 1.6 1.5 1.6 2.2 1.3 1.6 1.6 1.7 1.7 3.0 2.2 1.3 2.0 1.4 1.3 2.0 1.3 1.8 1.6 1.9 2.3 2.0 1.3 1.6 1.8 1.6 1.5 2.7 0.8 1.9 1.7 1.2 1.5 2.0 1.3 1.9 1.6 1.6 1.8 2.1 1.4 1.5 1.7 1.9 1.1 2.0 1.3 2.7 1.8 2.0 2.6 2.5 1.8 1.4 2.1 2.0 1.5 2.1 1.0 1.4 1.6 1.7 2.4 1.3 1.1 1.3 1.6 1.7 1.8 2.1 1.6 1.7 1.8 1.7 1.7 2.0 2.1 2.3 2.0 2.5.1.2 Discriminating power

The discriminating power of a sensory descriptor refers to the ability of a subject to differentiate the sounds with respect to its corresponding perceptual scale. For this purpose, analysis of variance (ANOVA) was used. This technique is a collection of statistical procedures, commonly used in design of experiments, in which the observed variance is attributed to various explanatory variables [22]. In sensory science, it is mainly used to describe the level at which the observed variance in the perceptual data can be attributed to a subject (listener), a product (sound), or a combination of both. In our case, one-way product analysis of variance was

conducted to verify the discriminating power of the sensory descriptors. Results are shown in Table 2.4. Except for descriptor D10, experts El, E2, E4 and E5 differentiated the sounds for all of the descriptors. The low reliability of expert E3 is translated in a lack of discriminating power, validating the relevance of 14 descriptors for the four other experts.

Table 2.4 Results of the one-way product ANOVA on the listeners' evaluations. White implies a significant effect for an interval of confidence of 95%; Light grey means a significant effect for an interval of confidence of

90% and dark grey shows an insignificant effect. Sensory descriptors Listener D l D2 D3 D4 D5 D6 D7 D8 D9 DIP D l l D12 D13 D14 D15 Expert 1 Expert 2 Expert 3 • j_ 'f""\ :.1 Expert 4 Expert 5 2.5.1.3 Inter-subject agreement

The last statistical validation of the 15 descriptors measured the mutual agreement between the evaluated perception between the listeners. A factorial discriminant analysis [25] applied to the mean assessments on the 15 descriptors led to the conclusion that the driving condition had a significant effect on the evaluation. Regarding this, two-way (product by subject) ANOVAs, including interactions, were thus conducted separately for each driving condition. The results were as follows, for a statistical confidence interval of 95%:

• For all descriptors, a significant subject effect indicated that the experts used either the low or the high part of the scale. Despite several training sessions, this scale effect was shown by significant subject effects.

• A significant product effect showed that the group perceived the 21 sounds as different, thus confirming the initial hypothesis stating that the sounds should present a notable diversity.

• On several descriptors, non-significant product-by-subject interaction effects indicated that the positioning of sounds in magnitude was identical among the listeners. However, disagreement within the experts occurred for the descriptors which showed high product-by-subject interaction effects. The 5 descriptors which showed the weakest agreement among the experts were eliminated.

From these results, it was clear that 10 of the 15 descriptors showed sufficient relevance for the selected panel of listeners.

2.5.2 Sensorial positioning of sounds

This section presents results summarizing the sensory information on a sensory map where the sounds are positioned according to their perceptual magnitudes. We conducted one PCA per driving condition, using the average magnitude outputs from listeners El, E2, E4 and E5 as the true perceptual magnitudes. The example of the "protruding surface driving condition" is illustrated by Figure 2.3. The two axes Fl and F2 (the two principal components) represent the two linear combinations of perceptual dimensions which contribute to most of the observed variance in the data. Each point represents the projection of a sound's perceptual magnitude on the principal plane. In this case, 81.11% of the variance between the magnitude ratings results from these two principal components (47.16% for the first one and 33.95% for the second). From the graph on the left, one can easily deduce that since the five automobiles (sounds) are well spread over the four quadrants of the plane, there were notable differences in the associated perception (they sound radically different). This confirms again the underlying hypothesis regarding the perceptual diversity of the recordings. In a case where all of the points would be grouped in the same quadrant, one could state that the sounds or the perceptual dimensions are not adequately discriminant to draw relevant conclusions from PCA.

The circular graph on the right shows the positioning of each descriptor (vector) plotted against principal components Fl and F2. If the length of such a vector is 1, then this descriptor can be completely quantified by a linear combination of Fl and F2. Vectors whose lengths are close to unity and which are collinear represent strong (negative or positive) correlation. In this example, D l l and D2 showed this behavior, as the information from one descriptor was redundant to the

other. However, since separate analyses were led for each driving condition, this may not mean that D l l and D2 are always redundant or synonyms.

a) Protruding_Panda Protruding_J77 Protruding_C85 Protruding_207 Protruding_J81 -4 0 4 F1 (47,16 %)

Figure 2.3 Results from the Principal Component Analysis on the protruding surface driving condition, a) sensorial positioning of the five sound samples on the principal plane. Values are projected magnitude scores

in centimeters, on a 15-cm scale, b) positioning of the 15 descriptors on the principal plane. Values are fractions of unity.

A more exhaustive interpretation of the various PCAs obtained in this road noise study can be found in [26]. The sensorial positioning of the sounds confirmed that in each condition, considerable differences between the sounds occurred, validating the variety of recorded noises. Moreover, the identification of the dominating sensory descriptors for one condition will be beneficial to the characterization of the vehicles regarding some specific quality target.

Through the results regarding individual repeatability and discriminating power it can be stated that four of the listeners showed sufficient performance to be considered as accurate measuring devices. The lack of inter-subject agreement on five sensorial descriptors reduced the list to 10.

2.6 Results of the regression analysis

This section presents the statistical results to the regression analysis linking sensory dimensions to (psycho-)acoustic metrics. Only the magnitudes collected in the evaluation stage, from the four accurate experts and the 10 relevant dimensions, were considered. For all perceptual

dimensions, only one or two objective metrics were included in the multiple regression analysis. Stepwise reduction algorithms are then unnecessary, and links can be found directly from signal processing. An example of the regression results for dimension D4 is shown in Figure 2.4.

a") Model-MeasurementComparison:Dimension D4 b ) Model-MeasurementComparison:Dimension D4

f i •= 1 S T " - - - " " - "

Figure 2.4 Example of the regression analysis on dimension D4, using one metric (SPL of an arbitrary band), a) comparison of mean assessments and predicted results on the 21 sounds, error bar : standard deviation

between the listeners, b) same data plotted in order of average rating.

In this example, the predicted data, using only one metric (sound pressure level of an arbitrary frequency band], always stand within one standard deviation from the panel's average assessment. As seen in Table 2.5, from the 10 selected perceptual dimensions, eight were quantified with coefficients of determination R2 greater than 0.8. In these cases, it can be

concluded that the calculated results account for at least 80% of the total variations observed. In all cases, p-values [25] lower than 0.05 confirmed that the correlation was conducted under high statistical confidence. Among the other two calculations, one (D12) depended upon systematic user input from an analyst. This situation occurred because calculating the tone-to-noise ratio required manual detection from a human being. The remaining dimension (D3) showed strong data polarization, which translated into an "yes/no" behavior and raised problems in regression. Using the described graphical optimization technique, the multiple regression analysis led to an optimal set of objective metrics which were correlated to the average perceived magnitudes on 8 of the 10 relevant perceptual dimensions.

Table 2.5 Coefficients of determination associated to the 10 proposed predictive calculations

Dimension R2 D1 0.89 D2 0.87 D3 0.44 D4 0.92 D13 0.96 D11 0.80 D9 0.83 D8 0.86 D12 N/A D14 0.93

2.7 Conclusion

This study has demonstrated a successful application of sensorial techniques to achieve a list of 10 perceptual dimensions describing the sound character of interior automotive road noise. The results have shown that the listeners' evaluations are repeatable, such that they assign the same magnitude ratings from a listening session to another. Furthermore, they differentiate the 21 road noises, since the sensory descriptors have a significant discriminating power. In spite of the differences observed between the experts, their level of precision was considered sufficient as an output of the sensory profile, for 4 experts and 10 perceptual dimensions. For eight of these, multiple linear regression allowed to identify a numerical model of the experts' perception (with R2 > 0.80).

This study helped creating a new on-track evaluation grid for the assessment of internal automotive road noise. In addition, a prediction of the expected perceived sound character parameters can now be accomplished through an associated software. In the near future, the sensory grid will require validation in real-life situations, as the sensory profile was based on room listening. The experts should evaluate the same vehicles in the same driving conditions as the sound recordings, and their assessments should be compared to those of this study.

In conclusion, coupling sensory methods with regression analysis allowed to significantly improve the understanding of the perception of internal road noise. From the associated software, a prediction of the human perception can be integrated to sound quality oriented design. Such a sensory technique can be extended to other acoustic components of a vehicle, or even to other human senses participating in global automotive quality assessment.

2.8 Acknowledgments

We would like to acknowledge the help of five experts from Renault for their implication in this work: Terry Alphonsine, Sonia Debant, Yannick Denoual, Kevin Dos-Santos and Laurent Galtier. Partial financial support from the Auto21 network is also acknowledged.

CONCLUSION

Ce projet de recherche avait pour but d'acquerir une meilleure connaissance de la perception humaine du bruit de roulement. Ce but general se declinait en trois sous-objectifs, soit le recensement des descripteurs pertinents, la liaison d'un calcul a chaque dimension perceptive associee et la proposition de nouvelles methodes devaluation suivant ces resultats.

Plusieurs etudes publiees montrent l'efficacite de techniques de regression lorsque les aspects acoustiques sont connus et evidents a l'oreille, ce qui n'est pas le cas pour le bruit de roulement automobile. Pour repondre a cette problematique, la presente recherche a employe une methodologie relativement nouvelle dans l'acoustique : le profil sensoriel classique.

Cette methode, developpee principalement pour 1'industrie alimentaire, a ete adaptee et appliquee a un jury de cinq experts acousticiens dans une salle de reecoute binaurale. Douze sessions de trois heures ont mene a l'elaboration d'une liste de dix descripteurs sensoriels. La pertinence de ceux-ci a ete validee par analyse de variance. Ces dix descripteurs permettent de distinguer les bruits de roulement de voitures particulieres dans quatre conditions de roulage typiquement rencontrees.

Ensuite, les dimensions perceptives associees a ces descripteurs ont ete comparees, par regression multiple, aux resultats de calculs de metriques sonores connues. De fortes correlations entre certaines metriques et les dimensions perceptives ont ete observees. Pour huit des dix dimensions perceptives, un calcul a ete propose pour predire la note d'intensite percue d'apres un enregistrement sonore.

Suivant ces resultats, deux nouvelles methodes d'evaluation des bruits de roulement ont ete proposees. D'abord, une fiche d'evaluation comprenant les dix dimensions perceptives a ete elaboree. Celle-ci pourra etre utilisee par un acousticien pour 1'evaluation in situ de la qualite sonore du bruit de roulement. Finalement, un logiciel integrant les correlations trouvees a ete concu pour predire les notes d'evaluation a partir du traitement de segments sonores enregistres dans une voiture roulant dans des conditions controlees.