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Perceptual characterisation of vehicules noises
Patrick Susini, Stephen Mcadams et Suzanne Winsberg
EEA Symposium: "Psychoacoustic in Industry and Universities", Janvier 1997
Copyright © Ircam - Centre Georges-Pompidou 1997
Version Française
Abstract
To improve sound quality in cars, a preference map was created on the basis of
several acoustic parameters relevant to auditory perception. A multidimensional
scaling technique (CLASCAL) was used to reveal common perceptual dimensions
shared by sets of car sounds, perceptual features specific to each sound, and
the different subject classes among listeners. The listeners' task was to judge
the degree of dissimilarity of all pairs of car sounds on a continuous scale.
The analysis gives a perceptual spatial representation of the sounds. From this
analysis, acoustic and auditory modelling analyses indicated that a number of
stimulus parameters were strongly correlated with different perceptual
dimensions and, where possible, with the specific features. A further
experiment investigated the probability of one sound being preferred to
another. An analysis of the data allowed a projection of the structure of
listeners' preferences onto the physical parameter space underlying the
previously determined multidimensional perceptual space. In many cases, it was
found that the physical parameters having the most effect on the listeners'
preferences were dependent on the set of stimuli being compared. Furthermore,
when one stimulus parameter was kept constant across trials, this altered the
effects of other parameters on the listeners' preferences.
1. Introduction
With the aim of improving the sound quality of car interiors, a psychoacoustic
study was performed at IRCAM in collaboration with two industrial partners,
Renault and PSA Peugeot Citroën. This study sought to determine
experimentally the perceptual structures underlying the comparison of car
sounds. Sixteen sounds were recorded with artificial heads on a test track with
a stable motor regime and were then presented binaurally to subjects under
controlled listening conditions. The first step consisted in determining the
perceptual attributes common to a group of subjects that judged the sounds with
respect to one another. A mutidimensional analysis with the CLASCAL model
(Winsberg & De Soete, 1993a) yielded a clear graphic representation that
revealed the perceptual structure underlying the judgments in terms of
continuous dimensions shared by all the stimuli and of characteristics specific
to each stimulus ("specificities"). The CLASCAL model also allows one to
account for different judgment strategies used by latent classes of subjects by
estimating the perceptual weights assigned to each dimension and the set of
specificities by each class. A subsequent phase of acoustic analyses sought to
determine the signal parameters that were correlated with the position of each
sound sample along each perceptual dimension. In a final stage, the degree of
judged preference associated with each sound as a function of the perceptually
significant parameters was estimated.
The perceptual representation model thus obtained provides constructors with a
tool with which the sound space of vehicle interiors can be improved according
to criteria associated with auditory quality.
2. Dissimilarity study
2.1. Method
2.1.1. Subjets and stimuli
In the dissimilarity study, 30 subjects aged from 25 to 45 years were
recruited. In order to study the influence of certain parameters on the
perceptual representation of car noises, four groups of stimuli were used in
all of the experiments. The cars were the same for each of the groups. Two
motor regimes were used (fixed motor and ground speed combinations). In
addition, for each regime, one acoustic parameter 1, was variable from
one stimulus to another in a first experiment series. In a second series, this
parameter was equalized over the set of stimuli. The composition of the four
groups of sounds is summarized in Table I.
|
Regime R1
|
Regime
R2
|
Parameter
1 variable
|
Group
1
|
Group
2
|
Parameter
1 constant
|
Group
3
|
Group
4
|
Table I : Composition of four groups of stimuli used.
2.1.4. Procedure
The experiment was conducted in four sessions with one session for each
stimulus group. All 120 pairs of different sounds for the 16 cars were
presented. At the beginning of the session, the subjects listened to all
samples for a given regime in a random order to get a sense of the range of
possible variation. Then 15 practice trials were presented to familiarize the
subject with the task. In each trial, the order of presentation of the pair of
samples was chosen randomly. A 1 s silence separated the two sounds which each
lasted about 5 s. The subject entered the dissimilarity judgment by moving a
slider on the computer screen that represented a continuous scale from very
similar at the left (coded 0) to very dissimilar at the right (coded 1). Each
sound pair could be replayed at will until the subject was satisfied with the
judgment. Once the judgment was entered, the next trial was automatically
presented.
2.2. Results
A Monte Carlo simulation was performed to determine the number of latent
classes that existed in the population of subjects tested. In our case, a
single class was sufficient to account for each stimulus group. Analyses for
from one to six dimensions without specificities and for from one to five
dimensions with specificities was performed for a single class of subjects
using the CLASCAL model. The information criterion BIC indicated that the best
solution had three dimensions with specificities for Group 3 and two dimensions
with specificities for the other stimulus groups. A graphic representation of
the solution for Group 3 stimuli is shown in Figure 1.
3. Physical parameters underlying the perceptual space
3.1. Acoustic analyses
A set of acoustic and psychoacoustic parameters (1, 2,
3, 4 and 5) was chosen by way of an empirical loop that
consisted in looking for parameters that were well correlated with the
coordinates of stimuli along a given dimension of the selected solution. Given
the quasi-stationary nature of the signals, the signal analyses were based on
the spectral analysis method of Welch.
3.2. Results
|
Groupe 1 |
Groupe 2 |
Param. |
Dim. 1 |
Dim. 2 |
Dim. 1 |
Dim. 2 |
1 |
-0.92* |
-0.06 |
-0.91* |
0.28 |
2 |
0.09 |
0.80* |
- |
- |
3 |
- |
- |
0.43 |
-0.88* |
|
Groupe 3 |
Groupe 4 |
Param. |
Dim. 1 |
Dim. 2 |
Dim. 3 |
Dim. 1 |
Dim. 2 |
2 |
0.35 |
-0.7* |
-0.14 |
-0.93* |
-0.29 |
3 |
- |
- |
- |
0.51+ |
0.86* |
4 |
-0.81* |
0.32 |
-0.33 |
- |
- |
5 |
-0.32 |
0.00 |
-0.83* |
- |
- |
Table II. Correlation coefficients between perceptual coordinates and
"objective" parameters. The probability p that the two measures are independent
is indicated for p < 0.01 (*) and for p < 0.05 (+).
In Table II, the correlation coefficients between the perceptual dimensions and
the acoustic or psychoacoustic parameters are shown for each stimulus group.
4. Preference study
4.1. Method
4.1.1. Subjets and stimuli
In this study, 60 subjects aged from 25 to 45 years were recruited. The stimuli
were the same as those used in the dissimilarity study.
4.1.2. Procedure
All pairs of different samples (in both orders) were presented in random order.
In each trial the subject heard the pair of samples once and was required to
chose which sound was preferred.
4.2. Preference analysis
For the set of subjects, a single triangular matrix without diagonal was
computed. An entry in the matrix corresponded to the probability that one
sample was preferred over the other. This probability is defined in the model
as a function of the difference in "utility" of the objective factors that
characterize the samples (Winsberg & De Soete, 1993b). In the analysis that
follows, these factors are the acoustic and psychoacoustic parameters that were
shown to have good correlations with the perceptual dimensions revealed in the
dissimilarity study. Since the perceptual spaces had two or three common
dimensions, we tested different pairs of corresponding parameters for
two-dimensional preference spaces. The analysis program searches for a function
that transforms the value of the objective parameter into a utility value that
depends on the empirical preference probability. This dependence is expressed
in the following way:
pi,j = (ui - uj), (1)
where pi,j is the probability that sample i will be preferred over
sample j, is the cumulative normal function, and ui is
the utility of sample i. As the difference in utility between the two samples
increases and is positive, the probability that sample i will be preferred over
sample j increases. The utility value is not identical to the preference
probability, but has a montonic relation to it.
4.3. Results
A graphic representation of the results allows a visualisation of the evolution
of utility of one stimulus in relation to another as a function of the
objective parameters determined in the dissimilarity analysis. In the simple
case where the sound pressure level would be a parameter revealed by such an
approach, its utility function may resemble that shown in Figure 2.
Figure 1. Perceptual space of the stimuli from Group 3.
Figure 2. Evolution of the utility of sound level.
5. Conclusion
In the first series of experiments performed with the parameter 1 not
being equalised, this parameter clearly dominated the preference judgments for
the two regimes, in spite of other parameters having equivalent degree of
effect on dissimilarity judgments. The secondary parameters varied with regime
and had a lesser importance for preference judgments. This indicates that equal
sensitivity in the case of dissimilarity judgments does not guarantee equal
effect in terms of preference. When the samples were equalized for parameter
1, other factors that contribute to preference emerge. In this case, no
single parameter dominated preference judgments across the two regimes. In
general, the perceptual salience of the dimensions was not the same for the two
regimes, which indicates that the relative importance of perceptual cues
evolves with regime and comparison context.
*This paper is based on an article submitted to the 4th French
Acoustics Congress, Marseille, April 1997, to appear in Journal de
Physique (Susini, McAdams & Winsberg, 1997).
References
Susini, P., McAdams, S. & Winsberg, S. (1997) Caractérisation
perceptive des bruits de véhicules. Journal de Physique (sous
presse).
Winsberg S. & De Soete G. (1993a) A latent class approach to fitting the
weighted Euclidean model, CLASCAL, Psychometrika, 58, 315-330.
Winsberg S. & De Soete G. (1993b) A Thurstonian pairwise choice model with
univariate and multivariate spline transformation, Psychometrika,
58, 233-256.
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