GEOMETRICALLY CONSTRAINED SOURCE EXTRACTION AND DEREVERBERATION BASED ON JOINT OPTIMIZATION

Yichen Yang, Xianrui Wang, Andreas Brendel, Wen Zhang, Walter Kellermann, Jingdong Chen

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

2 Scopus citations

Abstract

Source extraction, which aims at extracting the target source signals from the observed reverberant mixtures, plays an important role in voice communication and human-machine interfaces. Among the numerous source extraction methods that have been developed, the geometrically constrained (GC) one, which incorporates the direction-of-arrival (DOA) information of the target signals, has demonstrated great potential. However, this method generally suffers from significant performance degradation in strong reverberant environments since it is challenging to obtain in such environments accurate DOA estimates that are needed by the algorithm. To address this problem, we present in this work an iterative algorithm, which integrates the source-wise weighted prediction error (WPE)based dereverberation principle with the geometrically constrained source extraction method. We show that this algorithm is able to improve the DOA estimation accuracy as well as the source extraction performance.

Original languageEnglish
Title of host publication31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages41-44
Number of pages4
ISBN (Electronic)9789464593600
DOIs
StatePublished - 2023
Event31st European Signal Processing Conference, EUSIPCO 2023 - Helsinki, Finland
Duration: 4 Sep 20238 Sep 2023

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference31st European Signal Processing Conference, EUSIPCO 2023
Country/TerritoryFinland
CityHelsinki
Period4/09/238/09/23

Keywords

  • Blind source separation
  • dereverberation
  • geometrical constraint
  • semi-blind source extraction

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