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Conference Papers Year : 2003

Robust Speech Music Discrimination Using Spectrum's First Order Statistics and Neural Networks

Abstract

Most of speech/music discrimination techniques proposed in the literature need a great amount of training data in order to provide acceptable results. Besides, they are usually context-dependent. In this paper, we propose a novel technique for speech/music discrimination which relies on first order sound spectrum's statistics as feature vector and a neural network for classification. Experiments driven on 20000 seconds of various audio data show that the proposed technique has a great ability of generalization since a classification accuracy of 96% has been achieved only after a training phase on 80 seconds audio data. Furthermore, the proposed technique is context-independent as it can be applied to various audio sources.
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Dates and versions

hal-01587101 , version 1 (13-09-2017)

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Hadi Harb, Liming Chen. Robust Speech Music Discrimination Using Spectrum's First Order Statistics and Neural Networks. International Symposium on Signal Processing and its Applications, ISSPA 2003, Jul 2003, Paris, France. ⟨10.1109/ISSPA.2003.1224831⟩. ⟨hal-01587101⟩
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