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

Noboru Murata, Shiro Ikeda, Andreas Ziehe

研究成果: Article査読

381 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)1-24
ページ数24
ジャーナルNeurocomputing
41
1-4
DOI
出版ステータスPublished - 2001 1 1

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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