Sound quality evaluation based on artificial neural network

Authors:
Sang-Kwon Lee;Tae-Gue Kim;Usik Lee
Affiliations:
Acoustic Noise Signal Processing Labbatory, Dapartment of Mechanical Engineering, Inha University, Inchon, Korea;Acoustic Noise Signal Processing Labbatory, Dapartment of Mechanical Engineering, Inha University, Inchon, Korea;Acoustic Noise Signal Processing Labbatory, Dapartment of Mechanical Engineering, Inha University, Inchon, Korea
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Year:
2006

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Abstract

Booming index has been developed recently to evaluate the sound characteristics of passenger cars. Previous work maintained that booming sound quality is related to loudness and sharpness–the sound metrics used in psychoacoustics–and that the booming index is developed by using the loudness and sharpness for a signal within whole frequency between 20Hz and 20kHz. In the present paper, the booming sound quality was found to be effectively related to the loudness at frequencies below 200Hz; thus the booming index is updated by using the loudness of the signal filtered by the low pass filter at frequency under 200Hz. The relationship between the booming index and sound metric is identified by an artificial neural network (ANN).