Bayesian learning for speech dereverberation

Jen-Tzung Chien, You Cheng Chang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This study presents a Bayesian approach to enhance the magnitude spectra of single-channel reverberant speech signals. Speech dereverberation model is constructed by using a nonnegative convolutive transfer function (NCTF) and a nonnegative matrix factorization (NMF). NCTF is used to characterize the magnitude spectra of speech signal and room impulse response while NMF is applied to represent the fine structure of speech spectra. Importantly, we deal with the variations of dereverberation model by introducing the exponential priors for reverberation kernel and noise signal. A full Bayesian solution to speech dereverberation is obtained according to the variational Bayesian inference algorithm. Using this algorithm, the room configuration and the speaker characteristics are automatically learned from data. Such a general model can be reduced to the previous methods. Experimental results on both simulated data and real recordings from 2014 REVERB Challenge show the merit of the proposed method for single-channel speech dereverberation.

Original languageEnglish
Title of host publication2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
EditorsKostas Diamantaras, Aurelio Uncini, Francesco A. N. Palmieri, Jan Larsen
PublisherIEEE Computer Society
ISBN (Electronic)9781509007462
DOIs
StatePublished - 8 Nov 2016
Event26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy
Duration: 13 Sep 201616 Sep 2016

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2016-November
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
CountryItaly
CityVietri sul Mare, Salerno
Period13/09/1616/09/16

Keywords

  • Bayesian learning
  • nonnegative matrix factorization
  • speech dereverberation

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