NDLI: Design of new sound metric and its application for quantification of an axle gear whine sound by utilizing artificial neural network

Content Provider	Springer Nature Link
Author	Lee, Hyun Hoo Kim, Sung Jong Lee, Sang Kwon
Copyright Year	2009
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.
Starting Page	1182
Ending Page	1193
Page Count	12
File Format	PDF
ISSN	1738494X
Journal	Journal of Mechanical Science and Technology
Volume Number	23
Issue Number	4
e-ISSN	19763824
Language	English
Publisher	Korean Society of Mechanical Engineers
Publisher Date	2009-05-20
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Sound quality metric SNR index Axle whine noise Neural network Industrial and Production Engineering Vibration, Dynamical Systems, Control Mechanical Engineering
Content Type	Text
Resource Type	Article
Subject	Mechanics of Materials Mechanical Engineering

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