Blind source separation of more sources than mixtures using sparse mixture models

Zhenwei Shi, Huanwen Tang, Yiyuan Tang

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

In this paper, blind source separation is discussed with more sources than mixtures. This blind separation technique assumes a linear mixing model and involves two steps: (1) learning the mixing matrix for the observed data using the sparse mixture model and (2) inferring the sources by solving a linear programming problem after the mixing matrix is estimated. Through the experiments of the speech signals, we demonstrate the efficacy of this proposed approach.

Original languageEnglish
Pages (from-to)2491-2499
Number of pages9
JournalPattern Recognition Letters
Volume26
Issue number16
DOIs
StatePublished - Dec 2005

Keywords

  • Blind source separation
  • Independent component analysis
  • Overcomplete representation
  • Signal processing
  • Sparse mixture model

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