An approach to blind source separation based on temporal structure of speech signals

Noboru Murata, Shiro Ikeda, Andreas Ziehe

Research output: Contribution to journalArticle

370 Citations (Scopus)

Abstract

In this paper, we introduce a new technique for blind source separation of speech signals. We focus on the temporal structure of the signals. The idea is to apply the decorrelation method proposed by Molgedey and Schuster in the time-frequency domain. Since we are applying separation algorithm on each frequency separately, we have to solve the amplitude and permutation ambiguity properly to reconstruct the separated signals. For solving the amplitude ambiguity, we use the matrix inversion and for the permutation ambiguity, we introduce a method based on the temporal structure of speech signals. We show some results of experiments with both artificially controlled data and speech data recorded in the real environment.

Original languageEnglish
Pages (from-to)1-24
Number of pages24
JournalNeurocomputing
Volume41
Issue number1-4
DOIs
Publication statusPublished - 2001 Jan 1

Keywords

  • Blind source separation
  • Convolutive mixtures
  • Time-frequency domain

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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