A wavelet-based parameterization for speech/music segmentation
نویسندگان
چکیده
The problem of speech/music discrimination is a challenging research problem which significantly impacts Automatic Speech Recognition (ASR) performance. This paper proposes new features for the Speech/Music discrimination task. We propose to use a decomposition of the audio signal based on wavelets, which allows a good analysis of non stationary signal like speech or music. We compute different energy types in each frequency band obtained from wavelet decomposition. Two class/non-class classifiers are used : one for speech/non-speech, one for music/nonmusic. On the different test corpora, the proposed wavelet approach gives better results than the MFCC one. For instance, we have a significant relative improvements of the error rate of 58.0% on the “Scheirer” corpus for the speech/music discrimination task.
منابع مشابه
A wavelet-based parameterization for speech/music discrimination
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