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May 1990

Volume 87, Issue S1, pp. S1-S164

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back to top Session PP. Noise VI, Architectural Acoustics III, and Psychological Acoustics VI: Noise Quality I
Invited Papers
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History of noise quality in the automotive industry (A)

Earl Geddes

J. Acoust. Soc. Am. Volume 87, Issue S1, pp. S98-S98 (1990); (1 page)

Online Publication Date: 13 Aug 2005

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This paper will present a brief look at the emerging philosophy of sound quality as viewed from within the automotive engineering community. A few examples of the concepts of sound quality will be discussed focusing on their relevance to noise control in vehicles. The paper will be primarily historical in content relating how the ideas of sound quality came to be recognized as an important factor in noise control for automobiles.
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Psychoacoustics in context: Studying the listener and the noise (A)

Rebecca N Fleischman

J. Acoust. Soc. Am. Volume 87, Issue S1, pp. S98-S98 (1990); (1 page)

Online Publication Date: 13 Aug 2005

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Psychoacoustic studies of product sounds can be improved by a better understanding of what the listener brings to the evaluation of noise. Noise is interpreted in the context of a system of knowledge and beliefs about products and their operation. When correlating acoustic noise factors with listener judgments, ask the following questions about the meaning and context of noise: What are the expectations that listeners have about noise? Listeners may expect certain product‐specific sounds. They may judge product quality from specific noise features based on their beliefs about the relationship between the engineering or manufacturing of machines and their acoustic properties. What is the information content of a noise? Some components of a noise may be used as a diagnostic or to cue operation. What is the listening situation? Whether the listener is an active product operator or a passive bystander could mediate evaluations of noise quality. Examples of how these and other listener factors can influence noise judgments are presented, and strategies for incorporating them in psychoacoustic studies are discussed.
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Noise and meaning (A)

Jens P. Blauert

J. Acoust. Soc. Am. Volume 87, Issue S1, pp. S98-S99 (1990); (2 pages)

Online Publication Date: 13 Aug 2005

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Noise is undesired sound. The decision, however, of whether a sound is desired or undesired, is taken by the listener in the specific situation of sound exposure. A sound that is noise for one listener, may be a useful, information carrying signal for another listener and, moreover, each listener may change his mind at any time depending on the circumstances. Consequently, once a noise level is reduced so far that a physiological danger for the listeners' ear is no longer present, a further reduction of the level may not be the adequate measure to further decrease the negative impacts of the noise. Instead, a careful redesign of the complete acoustical environment at the listeners' position may be required. To this end, proper, ear‐adequate methods for noise measurement and noise quality evaluation are needed. In this talk, some developments along these lines, as currently being discussed in Germany, will be reviewed and evaluated.
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Ear‐adequate spectral analysis for noise evaluation (A)

Klaus Genuit

J. Acoust. Soc. Am. Volume 87, Issue S1, pp. S99-S99 (1990); (1 page)

Online Publication Date: 13 Aug 2005

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In numerous sound situations, sound quality cannot be determined by means of conventional measuring techniques. The frequent discrepancy between subjective judgment of sound events yielded by standard measurement procedures, can be explained by the difference between a sound situation as received by a single omnidirectional microphone, on the one hand, and as received by the human hearing, on the other hand. A new procedure using a dummy head and a special binaural analyzer has been developed for spectral analysis that realizes frequency and time resolution in the entire audio spectral range. This is comparable to the properties of human hearing, which is able to perceive quickly changing time structures and minor frequency differences at the same time. New approaches for ear‐adequate noise evaluation in order to judge noise quality can be achieved by means of psychoacoustic properties of human heating and binaural signal processing. The results of these investigations show that human hearing, which has two auditory paths with a complex directional characteristic and a binaural pattern recognition, gives a different judgment of noise quality as compared to conventional measurement methods, especially in complex sound situations with a spatial distribution of several sound sources and a sound‐pressure level below 85 dB.
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A method for evaluating interior sound quality of automobiles (A)

Tsuyoshi Yamashita, Yasuo Ishii, and Mitsuo Nakamura

J. Acoust. Soc. Am. Volume 87, Issue S1, pp. S99-S99 (1990); (1 page)

Online Publication Date: 13 Aug 2005

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In the past, the A‐weighted sound‐pressure level has been used to evaluate the noise level inside a vehicle cabin, since great importance is attached to reducing loudness. This report is an attempt to analyze sound “quality,” which is difficult to express solely in terms of loudness, using multivariate analysis performed on the results of a 20‐items semantic differential method survey. The results confirm that a vehicle's image bias has a greater effect on responses than actual differences in vehicle noise, thus clearly demonstrating the importance of a blindfold test. Proceeding from this result, blindfold tests were performed using the sound recorded from actual vehicles, both ordinary passenger cars and sports cars, and principal component analysis was performed on the results. Three principal tone factors were extracted: the “beauty factor,” the “powerfulness factor,” and the “metallicness factor.” The principal tone factors for automobile interior noise are similar to the results of studies done in other fields.
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Generating vehicle sounds synthetically (A)

Anthony J. Champagne, James C. Rogers, and Ray Wales

J. Acoust. Soc. Am. Volume 87, Issue S1, pp. S99-S99 (1990); (1 page)

Online Publication Date: 13 Aug 2005

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Synthetic vehicle sounds can be used for subjective listening tests. Computer models provide a great deal of flexibility while mechanical systems are not easily modified for such tests. Autoregressive (AR) parametric models were developed using real data for training. With this approach relatively few parameters are needed to model the vehicle sounds. Furthermore, arbitrarily long data sequences can be produced from these models for subjective evaluations by listeners. Interior recordings in two 4‐cylinder engine cars and in two 6‐cylinder engine cars were used as training data. It was found that signals with more complex spectra were modeled most successfully from the point of view of a listener. Signals with simple spectra (one dominant frequency component) were more difficult to model. Modeling difficulty manifests itself in amplitude variability with time in the sounds produced with the synthetic data. Measures to counter these effects have been met with some success. [Work supported by Ford Motor Company.]
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Signal processing techniques for identifying sound quality parameters (A)

P. Laux and P. Davies

J. Acoust. Soc. Am. Volume 87, Issue S1, pp. S99-S100 (1990); (2 pages)

Online Publication Date: 13 Aug 2005

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Importance is now being placed on the type of sound that machinery makes rather than simply on the noise level it produces. Two reasons for this emphasis are: the adverse affect an unpleasant acoustic environment can have on workers' performance; and the link a consumer makes between the quality of a machine and the sound it makes. The current problem is to find objective measures that reflect this subjective quantity known as sound quality. It is important to define such a quantity based on objective measures so that design objectives or performance specifications can be formulated rigorously. Single number indicators that are in use now include loudness, speech interference level, sound‐pressure level (A‐E weightings), and impact noise ratings. In the automobile industry, aspects of sound quality such as muddines, roughnes, and linearity have been shown to be correlated with the relative power of harmonic components in a spectrum and also to the statistics of the time history envelope. However, these indicators can yield similar values for signals that sound very different and cause a different subjective response. In order to identify the relationship between different signal components and the sound quality rating, techniques that can alter particular components of signals are needed to generate playback signals for subjective testing. In this paper, signal processing techniques are described that have been used to vary noise signal parameters so that components that contribute to sound quality can be identified.
Contributed Papers
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Analysis of objectionable noises in residential bedrooms (A)

Me Q. Wu, Bill Gastimeier, and D. L. Allen

J. Acoust. Soc. Am. Volume 87, Issue S1, pp. S100-S100 (1990); (1 page)

Online Publication Date: 13 Aug 2005

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This paper summarizes bedroom noise complaints collected by Vibron in the past 2 years. For the great majority of the 28 cases considered, the noise sources were mechanical equipment. Noise levels in most bedrooms where complaints occurred, were above NC 30. However, about 18% of the complaints came from bedrooms where noise levels were at or below NC 30. The lowest noise level was NC 20. A detailed study of the spectrum and the time signal of the low noise level cases indicates that the spectral and temporal characteristics as well as the background noise level affect the potential for annoyance. Peaks or discontinuities in either frequency or time domain usually cause complaints to noise with lower total levels. Lower background noise levels also increase the noise complaints.
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An objective correlation to subjective ratings of vehicle passenger compartment noise (A)

Kathleen K. Hodgdon

J. Acoust. Soc. Am. Volume 87, Issue S1, pp. S100-S100 (1990); (1 page)

Online Publication Date: 13 Aug 2005

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The intent of this research was to identify a more desirable sounding noise for the passenger compartment of the Pontiac 6000 LE and to establish an objective correlation to the subjective ratings of the automobile passenger compartment noise. The noise spectrum within the passenger compartment was acquired utilizing an artificial head during an acceleration run from a complete stop to maximum acceleration. The noise was then analyzed and the original noise sample and seven synthesized variations were paired in all possible combinations and randomly recorded for subjective presentation. Subjects were given a two‐alternative forced‐choice task in which they were instructed to choose either the first or second member of each pair as more desirable. Two independent objective procedures, the articulation index (AI) and Steven's Mark VII, were calculated based on the physical parameters of each noise spectrum. The eight noise samples were then rank ordered for desirability based on the subjective responses. The objective calculations for the spectra were rank ordered and compared to the subjective rankings. Statistical comparisons between the rankings, as well as the ability of the objective calculations to predict the subjective responses, were discussed.
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