Design of new sound metric and its application for quantification of an axle gear whine sound by utilizing artificial neural network |
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Authors: | Hyun-Hoo Lee Sung-Jong Kim Sang-Kwon Lee |
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Affiliation: | (1) Acoustc and Dynamics, Department of Mechanical Engineering, Inha University, Incheon, 402-751, Korea |
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Abstract: | The gear whine sound of an axle system is one of the most important sound qualities in a sport utility vehicle (SUV). Previous
work has shown that, because of masking effects, it is difficult to evaluate the gear whine sound objectively by using only
the A-weighted sound pressure level. In this paper, a new objective evaluation method for this sound was developed by using
new sound metrics, which are developed based on the increment of signal to noise ration and the psychoacoustic parameters
in the paper, and the artificial neural network (ANN) used for the modeling of the correlation between objective and subjective
evaluation. This model developed by using ANN was applied to the objective evaluation of the axle-gear whine sound for real
SUVs and the output of the model was compared with subjective evaluation. The results indicate a good correlation of over
90 percent between the subjective and objective evaluations.
This paper was recommended for publication in revised form by Associate Editor Yeon June Kang
Professor Sang-Kwon Lee received a Ph.D. degree in ISVR (Institute of Sound and Vibration Research) from Southampton University in 1998. He joined
Hyundai Motor Research Center in Korea, working with the Automotive Noise and Vibration Control Group from 1985 to 1994. He
has been the Professor at the Department of Mechanical Engineering, Inha University, Inchon, Korea, since March 1999. His
research interests are the digital signal processing, NVH (noise vibration harahness), condition monitoring, product sound
quality design and active control. |
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Keywords: | Sound quality metric SNR index Axle whine noise Neural network |
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