Multi-microphone speech dereverberation using expectation-maximization and Kalman smoothing

Boaz Schwartz, Sharon Gannot, Emanuel A.P. Habets

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

13 Scopus citations

Abstract

Speech signals recorded in a room are commonly degraded by reverberation. In most cases, both the speech signal and the acoustic system of the room are unknown. In this paper, a multi-microphone algorithm that simultaneously estimates the acoustic system and the clean signal is proposed. An expectation-maximization (EM) scheme is employed to iteratively obtain the maximum likelihood (ML) estimates of the acoustic parameters. In the expectation step, the Kalman smoother is applied to extract the clean signal from the data utilizing the estimated parameters. In the maximization step, the parameters are updated according to the output of the Kalman smoother. Experimental results show a significant dereverberation capabilities of the proposed algorithm with only low speech distortion.

Original languageEnglish
Title of host publication2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013
PublisherEuropean Signal Processing Conference, EUSIPCO
ISBN (Print)9780992862602
StatePublished - 2013
Event2013 21st European Signal Processing Conference, EUSIPCO 2013 - Marrakech, Morocco
Duration: 9 Sep 201313 Sep 2013

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference2013 21st European Signal Processing Conference, EUSIPCO 2013
Country/TerritoryMorocco
CityMarrakech
Period9/09/1313/09/13

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