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A psychoacoustic approach for sound quality assessment of automotive power windows

G. Volandri1, F. Di Puccio1, P. Forte1, C. Carmignani1, F. Becattini2

1 University of Pisa, Department of Mechanical, Nuclear and Production Engineering Largo Lazzarino 1, I-56100, Pisa, Italy e-mail: [email protected] 2 Magna Closures S.p.A, Motrol Division Via Francia 101, I-57010, Guasticce (Livorno), Italy

Abstract Psychoacoustic methodologies were investigated and applied to a case study within a cooperation project of the University of Pisa and Magna Closures S.p.A. mainly focused on the acoustics of power windows. The project included tests on closing-opening event sequences of the power window rail, to evaluate the component noise. Different trials were performed varying the test conditions. Time-frequency analyses, psychoacoustic metrics and models were applied to the Sound Pressure Level (SPL) time functions of the raw microphone signals and compared with threshold values from the literature. Scalar parameters quantifying the electric motor steadiness were also computed to characterize the noise excitation source while accelerometer signals were analysed in the frequency domain to extract modal parameters. The SPL time function appeared highly related to supply voltage, motor type and silent block insertion whereas psychoacoustic parameters were able to highlight further peculiarities of sound timbre.

1 Introduction

The perception of the noise emitted by automotive components is considered more and more crucial in customers’ assessment of the global quality of a car. Therefore, the prediction of customers’ expectations and satisfaction is the main aim of most acoustics and psychoacoustics studies from the Nineties, as well as the identification of the noise sources and a consequent aware design of components. In these terms, although it can appear as a minor point in the sound quality of a vehicle, the power window regulator is also gaining attention, particularly for high-end cars. This is confirmed by the rather broad literature on the acoustics of power windows [1-9], which is focused on some basic aspects of noise measurements as well as on advanced psychoacoustic criteria. In particular, the following critical points are dealt with

• the experimental set-up to test interior noise or automotive components [7];

• the objective and subjective characterization techniques of acoustic signals from automotive products [9];

• the main temporal and frequency features of power window noise [5];

• the identified noise mechanical sources and the consequent design guidelines oriented to noise limitation, including threshold values on some computable (also psychoacoustic) variables [5].

As far as analysis techniques for sound quality assessment are concerned, in particular for automotive products, they can be classified in several categories:

• subjective evaluation techniques in listening jury tests (e.g. individual preferences rating scales, and paired comparison tests) [7, 9-11];

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• psychoacoustic metrics (e.g. loudness, sharpness, roughness, fluctuation strength and Kurtosis) and models (e.g. Psychoacoustic Annoyance (PA)) [9];

• order analysis and indices to quantify the relative RPM deviation (e.g. motor DIP [1, 9]);

• time-frequency analysis techniques (e.g. fast (FFT) and short time (STFT) Fourier transform, spectrogram, power spectral density (PSD), third octave band spectrum, cepstrum [1, 5, 12], Wigner-Ville distribution and ambiguity function, wavelet transforms [13]);

• artificial neural networks and fuzzy logic, also combined with wavelet analysis [14];

• Computer Aided Engineering (CAE) models and, in particular, FE analysis, dynamic simulation of elastic bodies and lumped parameter models [15, 16].

Time-frequency analyses and psychoacoustic metrics/models appear to be the most suitable tools for an objective sound characterization, often used in the literature and in technical specifications as bases for defining threshold values. In order to develop predictive models of sound quality perception, the results of subjective jury tests are often combined and correlated with objective measurements (e.g. psychoacoustic metrics). To do that statistical means are generally employed to analyse results, such as “pair-wise T-test” confidence intervals, Pearson’s correlation coefficient, linear/non-linear single/multiple regression and determination coefficient and techniques such as factor analysis, principal component analysis, variance analysis and scatter plot [7, 11, 17]. This paper reports on the experimental activity carried out on a power window regulator, within a cooperation project between the Department of Mechanical, Nuclear and Production Engineering of the University of Pisa and Magna Closures S.p.A.. The application of a psychoacoustic approach in sound quality assessment of windows regulators, is only a part of a broader activity on noise carried out in this project, mainly oriented to support the manufacturer to define the criteria and a procedure for the design of a noiseless component. Several critical points were identified in this research, the first of which is the rather wide dispersion and low repeatability of measurements, due to the manufacturing and assembly processes, to the mutual interactions between the door and the power window regulator system and to the high variability of boundary conditions and operating environments. Another crucial element for manufacturers is related to the fact that, although the window regulator behavior is important, the acoustic response depends on the door and on the vehicle as well. Nonetheless the client’s final configuration of the door is usually not available in time for product development (the design of the door is frozen in parallel with that of and the window regulator). Therefore the main acoustic characteristics of the component that could affect the response of the whole system should be identified previously. In this sense it would be important also to properly define the client’s requirements and wishes by means of objective specifications (presently the evaluation is deferred to a jury’s judgment on the final product). The main objectives of the overall project are therefore:

• the definition of a test and data acquisition procedure, including the choice of the samples to be tested, that guarantees a good repeatability:

• the implementation of the defined procedure in a virtual environment for validating a calculation method based on the correlation between simulated and experimental data so as to predict the acoustic behavior of the window regulator mounted on the car door on the basis of its tested behavior as a single component.

• the definition of a series of psychoacoustic parameters that can make the client’s requirements as objective as possible.

The aim of this preliminary study is an investigation of the sound quality of power windows, comparing different configurations in order to select the most pleasant solution. For this purpose, psychoacoustic methodologies were considered and compared to other kind of approaches.

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2 Materials and Methods

2.1 Experimental activity

The experimental activity was carried out in two different sessions (S1 and S2), in a simple laboratory, not in anechoic nor hemi-anechoic chambers. However the tests were performed late in the afternoon, in order to have rather quiet conditions (around 40 dBA). Lab tests consisted in closing (traveling up)-opening (traveling down) event sequences to evaluate the component noise. The trials were performed varying the test conditions in terms of the type of electric motor (A or B), in the presence or not of silent blocks (SB), in the motor supply voltage, low or high (L, H); for each configuration, two or three repetitions were acquired. Moreover the component could be fixed to a car door which in turn was softly supported (Fig.1) or in a free-free configuration (Fig. 1) without the glass. Although the use of a sound vehicle with binaural measurements is more frequent in the literature (e.g. [5, 7]), in this case only a prototype of the door was available and therefore sound quality had to be inferred from a simpler set up that nonetheless was considered appropriate for comparative tests. In fact it should be noted that the design phases of the power window go together with the styling of the car and it is usually not possible to test it in the actual vehicle. However one can obtain indications for the component target performance from the client technical specifications and by numerical simulation of a virtual assembly. The instrumentation included a microphone, some micro-accelerometers, and a Hall transducer on the electric motor. Microphone signals, as well as a Hall transducer and accelerometer signals were acquired and analysed. The time-frequency analyses, psychoacoustic metrics were applied to the sound signal and compared with threshold values from the literature. Scalar parameters quantifying the electric motor steadiness were also computed to characterize the noise excitation source while accelerometer signals were analysed in the frequency domain to extract modal parameters.

Figure 1: Car door with window regulator (left) free-free configuration (right)

2.2 Data processing

Data processing consisted of analysis techniques partially deduced from a state of the art review. Some general analysis tools, available or purposely implemented in Matlab, were applied to the microphone and accelerometer signals to evaluate signal quality:

• the Sound Pressure Level (SPL) vs. time with "Fast" time exponential averaging, with reference value p0 = 20 μPa. A similar expression was employed for accelerometer signals with a reference

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value a0=10-6 m/s2. The SPL was expressed in dBA applying the A-weighting to account for the different sensitivity of the human ear in the frequency range. Temporal characteristics were distinguished into time segments (e.g. transient initial and stopping events and stationary traveling phase) by means of a semi-automatic identification procedure based on the SPL (dBA) function.

• auto- and cross-correlation to evaluate the repeatability of the experimental set-up;

• Signal to Noise Ratio (SNR), evaluated on the raw sound pressure time function or on the SPL (dBA), to estimate the ground noise;

• signal conditioning and filtering techniques, investigated to limit the measuring noise, to avoid aliasing problems in decimation procedures, to segregate the DC motor sound (< 1500 Hz) and most of the window seal scratching (> 1500 Hz) [18];

• time-frequency analyses and, in details: FFT of stationary signals;

• spectrogram, computed by means of STFT; PSD function;

• the third octave band spectrum (dB or dBA).

2.2.1 Analysis of the signal time phases

The analysis was focused only on the traveling up (window closure) as it is considered by car manufacturers most critical for acoustic annoyance. In fact in this phase the self-weight is a resistant load and the friction of the seals grows as the glass closes. It should be noted however that this is in contrast with the indications reported by Lim in [5], who investigated the opening phase in accordance to a preliminary jury evaluation test. Maybe the difference can be attributed to the different set-up (vehicle or door only). It is usually agreed that the opening/closure event can be split into three segments: a start and a stop phase separated by a quasi-steady one (Fig. 7a). Where not specified otherwise, we based the sound quality assessment only on the latter, excluding the initial and final transients, according to the approach proposed by [5]. However different criteria can be adopted to identify the boundaries between these segments; while Lim used the percentile loudness function, in our study we preferred to locate the selection cut at the local minimum of SPLA after/before the start/stop phase respectively (Fig. 7b).

0 2 4 6 8 10 12 14 16 18 20-1

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Microphone signal (Pa) SPLA (dBA)

up up down down

Time (s) Time (s) (a) (b)

Figure 2: Example of microphone signal in traveling up/down tests, session S2, motor B, H voltage; the raw signal of the two cycles (a), SPLA (b). The colors indicate the stationary traveling up phases


The following methodologies were applied to the microphone signal, focusing on the traveling up segment:

• Kurtosis function to quantify the squeak phenomenon;

• psychoacoustic metrics and models [5] and, in details: specific and total loudness (DIN 45631, ISO 532B) for stationary signals, time-varying loudness and loudness percentiles; total and specific sharpness, roughness and fluctuation strength; a psychoacoustic annoyance model, a combination of loudness percentile, sharpness, fluctuation strength and roughness metrics.

Scalar parameters were computed to characterize the DC motor velocity in the closing phases, monitored by a Hall transducer, since motor velocity variations appear to have a high influence on radiated noise:

• the percentage decrement ("motor DIP", [1] with respect to the mean value (RPM) of rotational velocity of the DC motor and translational speed of the glass;

• the Relative RPM Deviation (RRD), "steadiness" metrics [9].

2.2.2 Kurtosis

The squeak phenomenon related to the operation of a power window was noticed by [5] mainly at the start-up of the electric motor that is at the glass detachment from the seals, but it is also present in other phases of the glass motion. It is typically in the range from 3 to 8 kHz. A measure of squeak is Kurtosis K of the instantaneous sound pressure signal Pi(t), calculated as

4

4

( ( ) )Ni m

i

P t PKNσ

−= ∑ (1)

where Pm and σ are the mean value and standard deviation of Pi(t). Kurtosis is a measure of the difference in shape of the actual distribution with respect to the normal one, indicating more peakedness (K>0) or flattening (K<0) of the distribution. For the stationary phase of the power window motion, according to [5], values of K> 15 denote the presence of highly annoying phenomena whereas K<5 denotes the absence of squeak. Intermediate values indicate different levels of such a phenomenon that could be intolerable.

2.2.3 Psychoacoustic metrics and models

In this section the conventional psychoacoustic metrics [19] applied to the sound signals of this study are briefly described.

Loudness Loudness (N) is considered the metric which corresponds most closely to the sound intensity of a stimulus, whereas the psychoacoustic models combine more metrics. It is important to remark that the perception of loudness depends on source direction, bandwidth, frequency content and signal temporal characteristics [20]. The approximation of loudness with the SPL (dBA) is valid only for sinusoidal signals or narrow bandwidth and low levels. For stationary signals the more accredited model of loudness is the specific loudness N’, the subjective/perceived intensity that takes into account the critical band concept, i.e. the frequency interval within which two pure simultaneous tones cannot be perceived as distinct. In this work specific loudness and its integral, total loudness, were calculated as function of hearing 24 critical bands, according to the standard DIN 45631/ISO 532B [19].

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It is important to underline that specific and total loudness are conceived for stationary signals although they are used also for transients. This extension of loudness due to [21] to take into account time effects is known as “Time varying loudness” (tvN), according to DIN45631. The algorithm for time-varying signals calculates the third octave band spectrum almost instantaneously, performing an operation of exponential averaging with a time constant of 2 ms. Such a model was applied to the power window traveling up signals including transients and can be used as a reference to identify the different time phases [5]. In addition to loudness pattern and time function, statistical loudness distributions can be estimated, such as the percentile loudness Ni (i.e. loudness value reached or exceeded in i% of the measurement time). Mainly the N90 and N5 values are referred to in the literature [9]. In the power window application described in [5], peak values of time-varying loudness of a closing phase (i.e. stationary phase with initial and final transients) below 30 sone, included in the 30-35 sone range or above 35 sone are associated respectively to absence of negative impact, marginal and significant negative effects on perceived overall quality and annoyance.

Sharpness The sensation of sharpness (S) belongs to the timbre perception, but it is often considered separately [19]. It is related more to the spectral envelope and to the centre frequency of narrow-band sounds, rather than to the detailed spectrum structure. For narrow-band noises, sharpness increases with increasing centre frequency. In particular, for low centre frequencies, sharpness increases almost in proportion to critical-band rate whereas, at high frequencies, sharpness increases faster than the critical-band rate. Sharpness is highly dependent on bandwidth as it is evident, observing the sharpness of band-pass noise, that is a function of lower and upper cut-off frequency. Moreover the addition of noise at higher frequency increases sharpness while surprisingly adding sound components at lower frequencies decreases it. The sharpness metric, combined with loudness and relative RPM deviation of the electric motor, is considered a crucial factor in power window noise evaluation [9]. There are several methods to calculate sharpness; using the approach described in [19] sharpness can be calculated as the weighted first moment of the specific loudness over the critical-band rate.

Fluctuation strength and roughness Fluctuation strength (FS) and roughness (R) are metrics related to the hearing sensation of amplitude and frequency modulation for a modulation frequency <20 Hz and >20 Hz respectively. They have been applied in the evaluation of sound quality of electrical components although considered secondary parameters compared to loudness and sharpness. Specific (for the 24 critical bands) and total fluctuation strength and roughness were computed. The Aures's method [22] was adopted for roughness implementation.

Psychoacoustic models A psychoacoustic metric model can be required to represent an experienced perceptual dimension as a combination of metrics. The psychoacoustic elements and annoyance ratings of annoying sounds can be quantitatively described by psychoacoustic annoyance (PA), which is a combination of hearing sensations. Based on results of psychoacoustics experiments with modulated versus unmodulated narrow-band and broad-band sounds of different spectral distribution, a model of psychoacoustic annoyance was developed in [19] combining the information of percentile loudness N5, sharpness S, fluctuation strength FS and roughness R, according to the relations:


( )

2 25

0.45

5 1.

(1 );

1.75 0.25log( 10) 751.75

2.18 (0.4 0.6 )

0

S F

S

FR

R

S

S acuS acum

F RN

PA N

S N

ω ω

ω

ωω

= + +

= − ⋅

<

+

+ >

=

=

m (2)

Such a model represents a widely used index in sound quality evaluation but other models have been proposed specifically for power windows. In [9] a linear regression model based on loudness and sharpness well predicted the subjectively perceived annoyance while a model based on loudness, sharpness and speed variations of the electric motor well predicted the overall product quality perception, indicating that the speed variations of the electric motor are judged as a sign of bad quality.

3 Results

3.1 Preliminary analyses

3.1.1 Background noise analysis

Before carrying out the experimental tests, some preliminary controls on the DAQ quality were performed. The frequency analysis provided a noise spectrum characterized by the peak of the electrical net frequency (50 Hz) and its harmonics, and by low and high frequency components that could not be filtered by classical high-pass, low-pass or band-pass filters. Although alternative filtering methods operating on the entire frequency range were considered, we preferred in this early phase to keep all the frequency components of the signal since the range of interest partially overlaps that of the noise components. As far as the SNR, calculated for the sound pressure signal with respect to the background noise, we obtained critical values in several conditions. In the case of the microphone, the SNR is often evaluated on the SPL (dBA). Analysing the trend of the SPL (dBA) a difference of more than 10 dBA could be noted in the signal during the traveling up and down phases with respect to the background noise.

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Track 2 (27/04)Track 2 (27/04)Track 2 (27/04)Track 12 (1°) (10/06)Track 12 (2°) (10/06)

Figure 3: SPLA vs. time for the traveling up phase (closure) for set-up B/SB/H , sessions S1 and S2

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3.1.2 Repeatability analysis

As a preliminary aspect of the results of the tests on the component mounted on the door, the SPLA (dBA) functions of the only traveling up (closure) phase are plotted in Fig. 3. They are related to the same configuration (electric motor B with SB, supply voltage H), acquired in test sessions S1 and S2, aligned in time on the basis of the peak value. Figure 3 shows a good repeatability of measurements done in the same test session, while there are significant differences, especially in the time length of the traveling up phase, in different days. This can correspond to a variability of the window regulator itself; in fact although the component is nominally the same, some differences are unavoidable due to manufacturing and assembly processes. Moreover the corresponding third octave band spectra are reported in Fig. 4. Again a remarkable repeatability can be observed within the same test session, not confirmed considering different sessions.

Figure 4: Third octave band spectra of the traveling up phases (window closure) of the tests on the door

assembly for set-up B/SB/H, sessions S1 and S2 The functions of autocorrelation and cross-correlation and Pearson’s correlation index were calculated for the sound pressure rough signal and for the SPL (dBA), including or not the initial and end transients. For the rough signal low correlation values (ρ~0.2-0.3) were obtained for repeated tests within the same test session while no significant correlation (ρ<0.2) was observed for tests performed in different sessions. On the other hand a strong correlation (ρ>0.90) was found for the SPL (dBA) between tests performed within the same session as well as in different sessions, even though the former showed higher values (ρ>0.98). Such an analysis mainly indicates the need for standard test conditions as regards the environment, the measurement and acquisition set-up and the component fixture.

3.2 Tests on car door

In this section a comparison of different window regulator configurations mounted on the same car door is reported. As mentioned above, the differences lie in the type of electric motor (A or B), in the presence or not of SB, in the motor supply voltage (L, H). For the traveling up phase (closure), for sessions S1 and S2 respectively, Tab. 1 reports the calculated parameters relative to the time domain (traveling up and down time lengths, RMS value of sound pressure signal and average SPL dBA), psychoacoustic parameters (N, N5, N90, S, FS, R, PA, tvNmax (time-varying loudness maximum value)) and parameters related to the Hall sensor signal (motor DIP, RRD and the coefficient of Pearson’s correlation with the microphone signal).


Motor B B A A B B A A

Silent block SB SB SB SB ‐ ‐ ‐ ‐

Supply voltage H L H L H L H L

S1 2.86 3.48 2.00 2.79 2.67 3.34 1.95 2.23 Closing time (s) S2 2.32 2.88 2.11 2.58 2.39 3.13 2.14 2.55

S1 2.69 3.39 1.82 2.27 2.48 3.08 1.71 2.06 Opening time (s)

S2 2.44 2.78 1.90 2.29 2.50 3.10 1.92 2.38

S1 0.10 0.09 0.11 0.12 0.08 0.09 0.13 0.09 RMS (Pa) S2 0.04 0.06 0.06 0.07 0.05 0.05 0.10 0.07

S1 64.13 62.20 69.41 68.75 65.31 64.05 72.82 69.83 Mean SPL (dBA) S2 63.27 61.85 67.71 66.62 66.37 62.55 71.28 67.78

S1 72.31 71.25 77.85 76.57 69.86 68.91 79.10 79.90 Peak value (dBA)

S2 72.06 70.94 71.38 69.86 78.20 78.54 75.19 72.69

S1 62.25 60.92 67.23 64.52 63.72 62.37 71.60 66.49 Base value (dBA)

S2 61.96 60.44 65.75 64.63 63.62 60.56 68.73 65.63

S1 10.07 10.33 10.62 12.05 6.14 6.54 7.50 13.40 Peak‐base (dBA)

S2 10.10 10.50 5.63 5.23 14.58 17.98 6.46 7.06

S1 3.16 2.95 2.78 3.18 2.83 3.30 3.06 2.91 Kurtosis S2 2.87 2.66 2.99 2.95 2.78 2.28 2.79 2.78

S1 18.28 15.20 22.48 23.08 18.27 16.94 26.58 23.47 N S2 16.42 14.76 21.49 20.40 18.52 15.47 25.04 21.79

S1 1.59 1.64 1.53 1.35 1.82 1.65 1.48 1.54 S S2 1.41 1.42 1.65 1.56 1.54 1.53 1.64 1.59

S1 22.88 21.46 29.84 28.14 22.11 19.94 30.87 27.69 N5 S2 21.53 20.31 26.03 24.28 25.29 20.84 31.72 26.24

S1 4.44 8.44 12.54 7.54 6.56 4.81 5.03 4.73 N90 S2 3.13 4.31 2.94 5.29 4.74 3.48 3.49 5.73

S1 1.95 1.85 2.16 1.67 1.26 1.10 2.04 2.37 FS S2 2.18 2.26 1.76 1.23 2.91 3.86 1.61 1.35

S1 0.24 0.25 0.20 0.24 0.32 0.23 0.20 0.24 R S2 0.26 0.24 0.27 0.23 0.23 0.23 0.19 0.22

S1 36.03 33.63 46.29 41.26 31.83 27.53 46.79 45.21 PA S2 35.67 34.20 39.32 33.54 45.03 43.52 44.89 36.65

S1 29.94 28.25 36.82 34.04 24.84 22.81 39.61 41.43 tvNmax S2 27.52 30.55 30.82 31.13 39.00 36.83 35.44 31.44

S1 11.47 12.40 14.88 14.78 9.11 11.58 14.06 17.23 Motor Dip

S2 8.61 10.33 8.05 9.32 9.02 11.19 9.85 10.23

S1 11.13 12.10 14.44 14.38 9.01 11.38 13.66 16.61 RRD S2 8.45 10.08 7.92 9.15 8.87 10.98 9.67 10.03

S1 0.98 0.98 0.94 0.94 0.98 0.98 0.95 0.97 Pearson Coeff. (Max)

S2 0.95 0.97 0.96 0.98 0.96 0.96 0.95 0.96

Table 1: Results of different power window regulators in tests on door assembly for sessions S1 and S2. Parameters related to the traveling up phase. Blue/orange highlights the min/maximum value.

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Looking at Tab. 1 the following observations can be made:

• a rather wide scatter in measurements between the two sessions was observed and can be found in particular in the following parameter values: peak value, loudness percentile N90, time-varying loudness maximum value tvNmax, PA, motor DIP;

• the phase time length decreases as the supply voltage increases and is smaller in the case of motor A;

• the length of the traveling down phase (opening) is generally smaller than the length of the traveling up phase (closing);

• in both sessions, the average and minimum (base) SPL (dBA) values of the traveling up phases are higher in the case of motor A and without SB for both set-ups; such parameters as well as the peak of the SPL SPL (dBA) value increase as the supply voltage increases;

• the SPL (dBA) peak value of the traveling up phases for all set-ups is higher than the limit of 68 dBA indicated by [5];

• the difference between peak and minimum values of the SPL (dBA) signal of the traveling up stationary phase has unsettled trends for the two motors in the two sessions and has values alternatively lower or higher than 8 dBA; in most cases such a difference is higher at lower supply voltage;

• Kurtosis has an unsettled trend although it keeps a value below 5 for all set-ups in both sessions; that means no squeak according to [5];

• in both sessions total loudness and loudness percentile N5 are lower for motor B compared to motor A and, for the same motor, with SB; these parameters are in good agreement with RMS, mean SPL (dBA) and base values; the highest values of total loudness appear mostly due to the level reached in a limited time, as indicated by the correlation between N and N5;

• percentile N90, sharpness, roughness, fluctuation strength have an unsettled trend; one could observe that such noise characteristics are not discriminating and significant for this particular application;

• PA and time-varying loudness maximum value give similar indications: in session S1 they are lower for motor B while in session S2 they have lower values when silent blocks are present; moreover they increase as the supply voltage increases;

• time-varying loudness maximum value keeps below 30 sone [5] in case of motor B in session S1 and for set-up B/SB/H in session S2; in many cases it keeps between 30 and 35 sone, indicating a marginal annoyance [5];

• RRD and motor DIP appear correlated and in general higher at lower supply voltage; it is noteworthy that, in many cases, for lower supply voltage (L) such parameters exceed the reference value indicated by [7] by 10%; they exhibit no apparent relation with the other indices;

• the correlation between microphone signal SPLA and electric motor rotational speed appears in all cases very high (> 0.94); that confirms the hypothesis that glass speed variations due to load fluctuations or to geometric imperfections and clearances in the connections of the transmission system are sources of noise and vibrations.

• as the set-ups are concerned, it seems that, from an acoustic point of view, motor B is better than A, that SB improve the acoustic behavior, and generally low voltage should be applied.

3.3 Tests on the free-free component

The different window regulator configurations were also tested in session S1 in a free-free condition to investigate its own acoustic features, independently from the door/car characteristics. A correlation


between these two experimental set-up (car door/free free) could be useful for the design process of the component. Again the window regulator configurations differ for type of electric motor (A or B), the presence or not of SB, the electric motor supply voltage (L, H). The comparison can be done examining Tab. 2, which is based on the same parameters listed above for the tests on the door assembly

Motor B B A A B B A A

Silent block SB SB SB SB ‐ ‐ ‐ ‐

Supply voltage H L H L H L H L

Closing time (s) 2.55 3.07 1.58 2.00 2.65 2.83 1.81 2.14

Opening time (s) 2.63 3.09 1.71 2.00 2.63 2.89 1.79 2.26

RMS (Pa) 0.07 0.07 0.07 0.06 0.06 0.08 0.06 0.06

Mean SPL (dBA) 53.96 52.86 58.18 57.66 55.01 55.22 58.12 55.32

Peak value (dBA) 62.05 60.00 67.91 69.36 62.07 61.31 65.92 64.97

Base value (dBA) 52.84 51.65 57.02 56.61 53.78 53.98 56.97 54.48

Peak‐base (dBA) 9.22 8.36 10.89 12.75 8.29 7.33 8.95 10.49

Kurtosis 2.87 2.66 2.99 2.95 2.78 2.28 2.79 2.78

N 8.41 7.63 10.56 10.18 8.83 8.90 10.37 9.07

S 2.22 2.15 2.27 2.29 2.12 2.07 2.19 2.23

N5 9.85 8.91 12.82 12.12 10.24 10.52 11.96 11.14

N90 3.66 3.61 3.80 3.77 3.78 3.85 4.03 4.11

FS 1.35 1.13 1.84 2.17 0.85 0.75 1.73 2.28

R 0.20 0.36 0.31 0.14 0.19 0.18 0.22 0.25

PA 15.71 14.44 22.40 21.64 14.41 14.31 20.15 21.13

tvNmax 15.86 13.49 18.17 20.15 14.21 13.37 18.67 18.05

Motor Dip 4.89 6.27 2.58 2.75 2.39 2.40 4.22 5.43

RRD 4.87 6.24 2.58 2.76 2.39 2.41 4.20 5.40

Pearson Coeff. (Max) 0.97 0.97 0.99 0.97 0.98 0.98 0.99 0.97

Table 2: Results of different power window regulator configurations from tests on the free-free component

for sessions S1 and S2. Parameters related to the traveling up phase. Blue/orange highlights the min/maximum value.

Looking at Tab. 2, related to the only traveling up phases, the following observations can be made:

• the phase time length decreases as the supply voltage increases and it is smaller in the case of motor A;

• the lengths of the traveling up and down phases are similar due to the absence of the glass or equivalent weight;

• SPLA(dBA) average and peak values are ~10 dBA lower than in the tests on the door assembly;

• the average and minimum (base) SPLA values of the traveling up phases are higher in the case of motor A, for the same set-up; such parameters as well as the peak SPLA value generally increase as the supply voltage increases;

• the difference between peak and minimum values of the SPL (dBA) signal of the traveling up stationary phase has unsettled trends for the two motors and has values alternatively lower or higher than 8 dBA; however such a difference is lower for motor B;

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• Kurtosis has an unsettled trend although it keeps a value below 5 for all set-ups; that means no squeak according to [5];

• total loudness and loudness percentile N5 are lower for motor B compared to motor A, for the same set-up;

• percentile N90, sharpness, roughness, fluctuation strength have an unsettled trend;

• PA and time-varying loudness maximum value are lower for motor B;

• RRD and motor DIP are greatly reduced due to the absence of the glass or equivalent weight and are in general higher at lower supply voltage;

• the correlation between microphone signal SPLA electric motor rotational speed appears in all cases very high (> 0.97) although motor DIP and RRD seem not consistent with the other indices;

• as the configurations are concerned, again it seems that motor B is better than A, and low voltage should be applied while in this case the effect of SB is negligible.

3.4 Comparison between door and free-free set-ups

A correlation between the results of each window regulator configuration in the two experimental set-ups can be obtained by comparing columns of Table 1 (averaging the values of the two sessions) and Table 2; the following observations can be underlined:

• closing time generally increases in the door as the resistance is increased for the presence of the glass, while for the opening phase there is not a common trend;

• RMS is slightly reduced in the free-free set-up;

• the SPL (dBA) mean, peak and base values are reduced in the free-free configuration of about 10 dBA, therefore the difference peak-base is similar in the two cases;

• the total and percentiles loudness values, as well as the motor DIP and the RRD, are remarkably higher, as expected, in the door set-up (up to 50%),due to the contribution to noise emission of the door panel and to the glass inertia respectively;

• the total sharpness S shows an opposite behavior increasing for all configurations in the free-free experiments; this also can be explained with the presence of sound components at lower frequencies due to the door panel that has the effect of decreasing sharpness in the door set-up;

• PA mainly reflects the loudness trend. As far as the identification of the best/worst power regulator configuration is concerned, highlighted by colors in Tables 1-2, they generally do not correspond in the two experimental set-ups. In particular only the maximum and minimum closing/opening time are related to the same model in both cases, while for the mean, peak and base SPL, N, N5 and PA, the minimum values (best configuration) are found in the same power window configuration while the model with maximum values (worst configuration) is different. For the other parameters there is no apparent correlation between the two set-ups.

3.5 Accelerometer signal

The signals acquired from the accelerometers placed in different locations of the door assembly gave indications on the structural vibration frequencies and amplitudes to be related to the acoustic signal characteristics. Figure 5a shows an example of overlapped third octave band spectra (dBacc) of the traveling up phases (closure) for two accelerometers in session S2, set-up B/SB/H. In Fig. 5b the corresponding third octave band spectra of the microphone signal are reported.


(a)

(b)

Figure 5: Example of overlapping third octave band spectra of accelerometer (a) and microphone (b) signals respectively, for the traveling up phases, set-up B/SB/H on door assembly, session S2

One can see the obvious differences related to the accelerometer location and a good repeatability in the traveling up phases. The excitation frequencies typically correspond to the electric motor speed, in the low frequency range, while to the motor speed multiplied by the number of poles, in the high frequency range.

4 Conclusions

In this paper a preliminary case study on the sound quality of power window regulators is presented, part of a broader project on the design of this component. In particular an experimental investigation is described in which different configurations, in terms of motor type, presence or not of silent blocks, motor supply voltage, were tested to select the most pleasant solution. For this purpose, psychoacoustic methodologies were considered and compared to other kind of approaches Moreover two experimental set-ups were considered and their results compared: one with the component fixed to a car door and the other in a free-free configuration.

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The SPL (dBA) time function appeared highly related to supply voltage, motor type and silent block insertion. Psychoacoustic parameters such as loudness and loudness percentile N5 were in good agreement with classical parameters (i.e. RMS, mean SPL (dBA)) providing additional information on sound characteristics. PA and time varying loudness maximum value seemed able to highlight further peculiarities of sound timbre. However they appeared controversially related in different experimental sessions, suggesting a sensitivity study in order to define standard test conditions (e.g. environment, assembling and measurement system) and remarking the importance of preliminary quality checks and repeated acquisitions to increase the statistical significance. Other parameters such as Kurtosis, percentile N90, sharpness, roughness, fluctuation strength gave uncertain indications and were not able to discriminate between different set-ups and their sound characteristics, probably due to their marginal value in this particular application and product. Motor rotational speed variation parameters gave different indications as regards the best set-up with respect to the psychoacoustic indices. In our case, in fact, voltage appeared to have opposite effects on such parameters and, since low values of loudness, sharpness and motor speed variations are often associated to overall product high quality, an optimum solution appears difficult to find without suitably combining and weighting the different parameters. This operation, in order to be successful, must be done tuning the model with or according to the client’s sensitivity. Moreover, as shown by the tests on the single component compared to those on the door assembly, the results can differ a lot due to the acoustic effect of the door panel and masking phenomena. Therefore simulation on a computational modal and acoustic model of the component, validated by testing, and simulation on the assembly are essential for classifying the acoustic behavior of the component. Equally important is a jury test (with a jury carefully selected for the client’s target) to confirm the significance of the chosen acoustic and psychoacoustic parameters for power window sound quality assessment. Such activities are under way.

References

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