Using online model comparison in the variational bayes framework for online unsupervised voice activity detection

David Cournapeau, Shinji Watanabe, Atsushi Nakamura, Tatsuya Kawahara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper presents the use of online Variational Bayes method for online Voice Activity Detection (VAD) in an unsupervised context. In conventional VAD, the final step often relies on state machines whose parameters are heuristically tuned. The goal of this study is to propose a solid statistical scheme for VAD using online model comparison which is provided from the Variational Bayes framework. In this scheme, two models are estimated online in parallel: one for the noise-only situation , and the other for the noise-plus-signal situation The VAD decision is done automatically depending on the selected model. An experimental evaluation on the CENSREC-1-C database shows a significant improvement by the proposed method compared to conventional statistical VAD methods.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages4462-4465
Number of pages4
DOIs
Publication statusPublished - 2010 Nov 8
Externally publishedYes
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: 2010 Mar 142010 Mar 19

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CountryUnited States
CityDallas, TX
Period10/3/1410/3/19

Keywords

  • Robustness
  • Sequential estimation
  • Variational bayes
  • Voice activity detection

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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