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

Noboru Murata, Shiro Ikeda, Andreas Ziehe

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    357 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
    DOIs
    Publication statusPublished - 2001

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    Keywords

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

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Cellular and Molecular Neuroscience

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