Complex Extension of Infinite Sparse Factor Analysis for Blind Speech Separation

  • Kohei Nagira
  • Toru Takahashi
  • Tetsuya Ogata
  • Hiroshi G. Okuno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7191)


We present a method of blind source separation (BSS) for speech signals using a complex extension of infinite sparse factor analysis (ISFA) in the frequency domain. Our method is robust against delayed signals that usually occur in real environments, such as reflections, short-time reverberations, and time lags of signals arriving at microphones. ISFA is a conventional non-parametric Bayesian method of BSS, which has only been applied to time domain signals because it can only deal with real signals. Our method uses complex normal distributions to estimate source signals and mixing matrix. Experimental results indicate that our method outperforms the conventional ISFA in the average signal-to-distortion ratio (SDR).


Blind source separation Infinite sparse factor analysis Non-parametric Bayes 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kohei Nagira
    • 1
  • Toru Takahashi
    • 1
  • Tetsuya Ogata
    • 1
  • Hiroshi G. Okuno
    • 1
  1. 1.Graduate School of InformaticsKyoto UniversityKyotoJapan

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