A hypothesis testing approach for real-time multichannel speech separation using time-frequency masks

Ryan M. Corey, Andrew C. Singer

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

Abstract

We propose a new approach to time-frequency mask generation for real-time multichannel speech separation. Whereas conventional approaches select the strongest source in each time-frequency bin, we perform a binary hypothesis test to determine whether a target source is present or not. We derive a generalized likelihood ratio test and extend it to underdetermined mixtures by aggregating the outputs of several tests with different interference models. This approach is justified by the nonstationarity and time-frequency disjointedness of speech signals. This computationally simple method is suitable for real-time source separation in resource-constrained and latency-critical applications.

Original languageEnglish (US)
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 - Nov 8 2016
Event26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy
Duration: Sep 13 2016Sep 16 2016

Publication series

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

Other

Other26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
CountryItaly
CityVietri sul Mare, Salerno
Period9/13/169/16/16

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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