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.
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