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Feature analysis and selection for acoustic event detection
Conference proceeding

Feature analysis and selection for acoustic event detection

Xiaodan Zhuang, Xi Zhou, Thomas S. Huang, Mark Hasegawa-Johnson, IEEE and Xiaohua Hu
2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12, pp 17-20
01 Jan 2008

Abstract

Acoustics Computer Science Computer Science, Artificial Intelligence Computer Science, Cybernetics Engineering Engineering, Biomedical Engineering, Electrical & Electronic Imaging Science & Photographic Technology Life Sciences & Biomedicine Mathematical & Computational Biology Radiology, Nuclear Medicine & Medical Imaging Science & Technology Technology Telecommunications
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.

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Web of Science research areas
Acoustics
Computer Science, Artificial Intelligence
Computer Science, Cybernetics
Engineering, Biomedical
Engineering, Electrical & Electronic
Imaging Science & Photographic Technology
Mathematical & Computational Biology
Radiology, Nuclear Medicine & Medical Imaging
Telecommunications
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