Online unsupervised classification with model comparison in the variational bayes framework for voice activity detection

David Cournapeau*, Shinji Watanabe, Atsushi Nakamura, Tatsuya Kawahara

*この研究の対応する著者

研究成果: Article査読

11 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号5586640
ページ(範囲)1071-1083
ページ数13
ジャーナルIEEE Journal on Selected Topics in Signal Processing
4
6
DOI
出版ステータスPublished - 2010 12月
外部発表はい

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学

フィンガープリント

「Online unsupervised classification with model comparison in the variational bayes framework for voice activity detection」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル