Measuring Dependence for Permutation Alignment in Convolutive Blind Source Separation

Baoze Ma, Tianqi Zhang, Zeliang An, Chen Yi

Research output: Contribution to journalJournal articleResearch

5 Citations (Scopus)

Abstract

This brief proposes an effective implementation for addressing permutation ambiguity issue of convolutive blind source separation in frequency domain. Generally, signal envelope and power ratio as common inter-frequency dependence measures are utilized to group bin-wise separated signals for convolutive mixtures, where the new measure of permutation alignment method is represented by the activation matrix of bin-wise spectrum which is based on nonnegative matrix factorization (NMF). Meanwhile, canonical correlation analysis (CCA) rather than the maximum sum of correlation coefficient among different bins, is applied for verifying correlation determinations. In addition, the influence of bin distance and separation quality at each bin are explored to optimize permutation result. Simulation results demonstrate the effectiveness of the proposed technique in real recorded convolutive mixtures.

Original languageEnglish
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Volume69
Issue number3
Pages (from-to)1982-1986
Number of pages5
ISSN1549-7747
DOIs
Publication statusPublished - Mar 2022
Externally publishedYes

Keywords

  • Convolutive blind source separation
  • canonical correlation analysis
  • nonnegative matrix factorization
  • permutation alignment

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