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Classification of audio events using permutation transformation |
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| Affiliation: | 1. Institute of Biomaterials and Biomedical Engineering, University of Toronto, Rosebrugh Building, 164 College St., Room 407, Toronto, Ont. M5S 3G9, Canada;2. Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, 150 Kilgour Road, Toronto, Ont. M4G 1R8, Canada;1. Gongqing College, Nanchang University, Jiujiang 332020, China;2. Faculty of Mathematics and Statistics, Hubei University, Wuhan 430062, China;3. School of Information Engineering, Nanchang University, Nanchang 330031, China;1. LR-SITI Laboratory, National Engineering School of Tunis, Tunis, Tunisia;2. Arab University of Science, Tunisia |
| Abstract: | Automatic detection and classification of short and nonstationary events in noisy signals is widely considered to be a difficult task for traditional frequency domain and even time–frequency domain approaches. A novel method for audio signal classification is introduced. It is based on statistical properties of the temporal fine structure of audio events. Artificially generated random signals and unvoiced stop consonants of speech are used to evaluate the method. The results show improved recognition accuracy in comparison to traditional approaches. |
| Keywords: | Stop consonant recognition Temporal fine structure Permutation transformation Feature extraction Audio event detection Pattern recognition |
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