ICANN 98 pp 761-766 | Cite as

An Approach to Blind Source Separation of Speech Signals

  • Shiro Ikeda
  • Noboru Murata
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)


In this paper we introduce a new technique for blind source separation of speech signals. We focused on the temporal structure of signals which is not always the case in other major approaches. The idea is to apply the decorrelation method proposed by Molgedey and Schuster in time-frequency domain. We show some results of experiments with artificial data and speech data recorded in the real environment. Our algorithm needs considerably straightforward calculation and includes only a few parameters to be tuned.


Speech Signal Window Length Blind Source Separation Artificial Data Separate Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    J.-F. Cardoso and A. Souloumiac. Jacobi angles for simultaneous diagonalization. SIAM J. Mat. Anal. Appl, 17(1):161–164, 1996.MathSciNetMATHCrossRefGoogle Scholar
  2. [2]
    S.C. Douglas and A. Cichocki. Neural networks for blind decorrelation of signals. IEEE Trans. Signal Processing, 45(11):2829–2842, 1997.CrossRefGoogle Scholar
  3. [3]
    T.-W. Lee, A. Ziehe, R. Orglmeister, and T. Sejnowski. Combining time-delayed decorrelation and ICA: towards solving the cocktail party problem. In Proceedings of ICASSP’98, 1998.Google Scholar
  4. [4]
    L. Molgedey and H.G. Schuster. Separation of a mixture of independent signals using time delayed correlations. Phys. Rev. Lett, 72(23):3634–3637, 1994.CrossRefGoogle Scholar
  5. [5]
    P. Smaragdis. Blind separation of convolved mixtures in the frequency domain. In International Workshop on Independence & Artificial Neural Networks, University of La Laguna, Tenerife, Spain, 1998.Google Scholar
  6. [6]
    A. Ziehe. Statistische verfahren zur signalquellentrennung. Master’s thesis, Humboldt Universität, Berlin, 1998. (in German).Google Scholar

Copyright information

© Springer-Verlag London 1998

Authors and Affiliations

  • Shiro Ikeda
    • 1
  • Noboru Murata
    • 1
  1. 1.Brain Science InstituteRIKENWakoJapan

Personalised recommendations