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Cognitive Computation

Erschienen in:

01.09.2010

A Non-Linear VAD for Noisy Environments

verfasst von: Jordi Solé-Casals, Vladimir Zaiats

Erschienen in: Cognitive Computation | Ausgabe 3/2010

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Abstract

This paper deals with non-linear transformations for improving the performance of an entropy-based voice activity detector (VAD). The idea to use a non-linear transformation has already been applied in the field of speech linear prediction, or linear predictive coding, based on source separation techniques, where a score function is added to classical equations in order to take into account the true distribution of the signal. We explore the possibility of estimating the entropy of frames after calculating its score function, instead of using original frames. We observe that if the signal is clean, the estimated entropy is essentially the same; if the signal is noisy, however, the frames transformed using the score function may give entropy that is different in voiced frames as compared to unvoiced ones. Experimental evidence is given to show that this fact enables voice activity detection under high noise, where the simple entropy method fails.

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Titel: A Non-Linear VAD for Noisy Environments
verfasst von: Jordi Solé-Casals
Vladimir Zaiats
Publikationsdatum: 01.09.2010
Verlag: Springer-Verlag
Erschienen in: Cognitive Computation / Ausgabe 3/2010
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI: https://doi.org/10.1007/s12559-010-9037-4

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