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Feature analysis and selection for acoustic event detection

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4 Author(s)
Xiaodan Zhuang ; Beckman Institute of Advanced Science & Technology, Department of Electrical & Computer Engineering, University of Illinois Urbana-Champaign, 61801, USA ; Xi Zhou ; Thomas S. Huang ; Mark Hasegawa-Johnson

Speech perceptual features, such as Mel-frequency Cepstral Coefficients (MFCC), have been widely used in acoustic event detection. However, the different spectral structures between speech and acoustic events degrade the performance of the speech feature sets. We propose quantifying the discriminative capability of each feature component according to the approximated Bayesian accuracy and deriving a discriminative feature set for acoustic event detection. Compared to MFCC, feature sets derived using the proposed approaches achieve about 30% relative accuracy improvement in acoustic event detection.

Published in:

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

Date of Conference:

March 31 2008-April 4 2008