Blind speech dereverberation using batch and sequential Monte Carlo methods

Christine Evers, James R. Hopgood, Judith Bell

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

3 Citations (Scopus)

Abstract

Reverberation and noise cause significant deterioration of audio quality and intelligibility to signals recorded in acoustic environments. Bayesian dereverberation infers knowledge about the system by exploiting the statistical properties of speech and the acoustic channel. In Bayesian frameworks, the signal can be processed either sequentially using online methods or in a batch using offline methods. This paper compares the two approaches for blind speech dereverberation by means of a previously proposed batch approach and a novel sequential approach. Results show that while both methods have different advantages, online processing leads to a more flexible solution. ©2008 IEEE.

Original languageEnglish
Title of host publication2008 IEEE International Symposium on Circuits and Systems, ISCAS 2008
Pages3226-3229
Number of pages4
DOIs
Publication statusPublished - 2008
Event2008 IEEE International Symposium on Circuits and Systems - Seattle, WA, United States
Duration: 18 May 200821 May 2008

Conference

Conference2008 IEEE International Symposium on Circuits and Systems
Abbreviated titleISCAS 2008
CountryUnited States
CitySeattle, WA
Period18/05/0821/05/08

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  • Cite this

    Evers, C., Hopgood, J. R., & Bell, J. (2008). Blind speech dereverberation using batch and sequential Monte Carlo methods. In 2008 IEEE International Symposium on Circuits and Systems, ISCAS 2008 (pp. 3226-3229) https://doi.org/10.1109/ISCAS.2008.4542145