Blind separation of convolutive mixtures of speech sources: Exploiting local sparsity

Xiao Fu, Wing Kin Ma

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

2 Scopus citations

Abstract

This paper presents an efficient method for blind source separation of convolutively mixed speech signals. The method follows the popular frequency-domain approach, wherein researchers are faced with two main problems, namely, per-frequency mixing system estimation, and permutation alignment of source components at all frequencies. We adopt a novel concept, where we utilize local sparsity of speech sources in transformed domain, together with non-stationarity, to address the two problems. Such exploitation leads to a closed-form solution for per-frequency mixing system estimation and a numerically simple method for permutation alignment, both of which are efficient to implement. Simulations show that the proposed method yields comparable source recovery performance to that of a state-of-the-art method, while requires much less computation time.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages4315-4319
Number of pages5
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

Keywords

  • Blind Source Separation
  • Convolutive Mixture
  • Permutation Ambiguity
  • Speech Separation

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  • Cite this

    Fu, X., & Ma, W. K. (2013). Blind separation of convolutive mixtures of speech sources: Exploiting local sparsity. In 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings (pp. 4315-4319). [6638474] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2013.6638474