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Nonstationary source separation for underdetermined speech mixtures

  • University of Illinois at Urbana-Champaign

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

4 Scopus citations

Abstract

We propose a multichannel source separation method for underdetermined mixtures of nonstationary signals, such as speech. Like other underdetermined algorithms, our method relies on the time-frequency sparsity of speech. However, our interference model allows more than one source to be active at the same time and frequency, providing better separation performance for mixtures of many sources. The system consists of several beamformers designed for different combinations of interference sources. A decision rule selects the beamformer that best suppresses the active interferers at each time-frequency point. Experiments on both simulated and real mixtures show improved interference suppression compared to conventional beamformers.

Original languageEnglish
Title of host publicationConference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages934-938
Number of pages5
ISBN (Electronic)9781538639542
DOIs
StatePublished - Mar 1 2017
Event50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States
Duration: Nov 6 2016Nov 9 2016

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Conference

Conference50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Country/TerritoryUnited States
CityPacific Grove
Period11/6/1611/9/16

Keywords

  • array processing
  • beamforming
  • microphone arrays
  • nonstationarity
  • Source separation
  • speech enhancement

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