SAE Technical Paper Series 1999
DOI: 10.4271/1999-01-1721
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Gear Noise Rating Prediction Based on Objective Measurement

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Cited by 6 publications
(9 citation statements)
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“…Basically, the interior sound of a car consists of very complex frequency spectrum since it has many excitation sources, resonance systems and parts of sound radiation [1][2][3][4]. However, it is known that the meshing frequency of the gear in the axle system influences the axle-gear whine sound quality [5][6][7]. Other frequency components play roles of background noise.…”
Section: Synthetic Axle-gear Whine Soundmentioning
confidence: 99%
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“…Basically, the interior sound of a car consists of very complex frequency spectrum since it has many excitation sources, resonance systems and parts of sound radiation [1][2][3][4]. However, it is known that the meshing frequency of the gear in the axle system influences the axle-gear whine sound quality [5][6][7]. Other frequency components play roles of background noise.…”
Section: Synthetic Axle-gear Whine Soundmentioning
confidence: 99%
“…In particular, the axle-gear whine sound in a sport utility vehicle (SUV) becomes one of the dominant sound sources as the number of SUVs increases worldwide. It is sometimes difficult to evaluate the axle-gear whine sound objectively from the viewpoint of sound quality since it is embedded in the background sounds [6]. In this paper, considering this masking effect, a new metric is devel oped and used for the objective evaluation of the axlegear whine sound.…”
Section: Introductionmentioning
confidence: 99%
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“…In this sense, the axle-gear whine sound, which is originally masked by the dominant noise sources such as the internal combustion engine and transmission system, can become one of the dominant noise sources in a sports utility vehicle (SUV). [7][8][9] It needs to be reduced to a reasonable level in order to improve the interior sound quality. For this reason, the previous studies have focused on how to properly evaluate the tonality of perceived sounds, and to develop a sound quality index for the axle-gear whine sounds.…”
Section: Introductionmentioning
confidence: 99%
“…It was experimentally confirmed that the effects of tonality on the annoyance model mainly vary as a function of amplitude and frequency. [8][9][10] Therefore, it was expected that Aures' tonality, which has been used for expressing the tonality of sound attributes, would have enough correlation with the mean response of participants, and could be used as the input data, with the loudness, in the training process of an artificial neural network (ANN). ANNs have been used for the modelling of the correlation between objective and subjective evaluation.…”
Section: Introductionmentioning
confidence: 99%