Blind source separation of more sources than mixtures using generalized exponential mixture models

Zhenwei Shi, Huanwen Tang, Wenyu Liu, Yiyuan Tang

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

Blind source separation is discussed with more sources than mixtures in this paper. The blind separation technique includes two steps. The first step is to estimate a mixing matrix, and the second is to estimate sources. If the sources are sparse, the mixing matrix can be estimated by using the generalized exponential mixture model. The generalized exponential mixture model is a powerful uniform framework to learn the mixing matrix for sparse sources. A gradient learning algorithm for the generalized exponential mixture model is derived. After estimating the mixing matrix, the sources can be obtained by using the maximum a posteriori approach. The speech-signal experiments demonstrate effectiveness of the proposed approach.

Original languageEnglish
Pages (from-to)461-469
Number of pages9
JournalNeurocomputing
Volume61
Issue number1-4
DOIs
StatePublished - Oct 2004

Keywords

  • Blind source separation
  • Generalized exponential mixture model
  • Independent component analysis
  • Overcomplete representation

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