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Title: Online Unsupervised Classification with Model Comparison in the Variational Bayes Framework for Voice Activity Detection
Authors: Cournapeau, David
Watanabe, Shinji
Nakamura, Atsushi
Kawahara, Tatsuya  kyouindb  KAKEN_id  orcid (unconfirmed)
Author's alias: 河原, 達也
Issue Date: Dec-2010
Publisher: IEEE
Journal title: IEEE Journal of Selected Topics in Signal Processing
Volume: 4
Issue: 6
Start page: 1071
End page: 1083
Abstract: A new online, unsupervised method for Voice Activity Detection (VAD) is proposed. The conventional VAD methods often rely on heuristics to adapt the decision threshold to the estimated SNR. The proposed VAD method is based on the Variational Bayes (VB) approach to the online Expectation Maximization (EM), so that it can automatically adapt the decision level and the statistical model at the same time. We consider two parallel classifiers, one for the noise-only case, and the other for speech-and-noise case. Both models are trained concurrently and online using the VB framework. The VB framework also provides an explicit approximation of the log evidence called free energy. It is used to assess the reliability of the classifier in an online fashion, and to decide which model is more appropriate at a given time frame. Experimental evaluations were conducted on the CENSREC-1-C database designed for VAD evaluations. With the effect of the model comparison, the proposed scheme outperforms the conventional VAD algorithms, especially in the remote recording condition. It is also shown to be more robust with respect to changes of the noise type.
Rights: © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
DOI(Published Version): 10.1109/JSTSP.2010.2080821
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