K-means Based Underdetermined Blind Speech Separation

  • Shoko Araki
  • Hiroshi Sawada
  • Shoji Makino
Chapter
Part of the Signals and Communication Technology book series (SCT)

This chapter addresses a blind sparse source separation method that can employ arbitrarily arranged multiple microphones. Some sparse source separation methods, which rely on source sparseness and an anechoic mixing model, have already been proposed. The validity of the sparseness and anechoic assumptions will be investigated in this chapter. As most of the existing methods utilize a stereo (two sensors) system, they limit the separation ability to a 2-dimensional half-plane. This chapter describes a method for multiple microphones. This method employs the k-means algorithm, which is an efficient clustering algorithm. The method can be easily applied to three or more sensors arranged nonlinearly. Promising results were obtained for 2- and 3-dimensionally distributed speech signals with nonlinear/nonuniform sensor arrays in a real room even in underdetermined situations.

Keywords

Independent Component Analysis Speech Signal Blind Source Separation Frequency Point Binary Mask 
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.
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References

  1. 1.
    S. Haykin, Ed., Unsupervised Adaptive Filtering (Volume I: Blind Source Sep-aration). John Wiley & Sons, 2000.Google Scholar
  2. 2.
    A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis. John Wiley & Sons, 2001.Google Scholar
  3. 3.
    Ö . Yılmaz and S. Rickard, “Blind separation of speech mixtures via time-frequency masking,” IEEE Trans. on SP, vol. 52, no. 7, pp. 1830-1847, 2004.CrossRefGoogle Scholar
  4. 4.
    H. Buchner, R. Aichner, and W. Kellermann, “Blind source separation for con-volutive mixtures: A unified treatment,” in Audio Signal Processing for Next-Generation Multimedia Communication Systems, Y. Huang and J. Benesty, Eds. Kluwer Academic Publishers, Feb. 2004, pp. 255-293.Google Scholar
  5. 5.
    H. Sawada, R. Mukai, S. Araki, and S. Makino, “Frequency-domain blind source separation,” in Speech Enhancement, J. Benesty, S. Makino, and J. Chen, Eds. Springer, Mar. 2005, pp. 299-327.Google Scholar
  6. 6.
    S. Amari, S. Douglas, A. Cichocki, and H. Yang, “Multichannel blind decon-volution and equalization using the natural gradient,” in Proc. IEEE Workshop on Signal Processing Advances in Wireless Communications, Apr. 1997, pp. 101-104.Google Scholar
  7. 7.
    P. Smaragdis, “Blind separation of convolved mixtures in the frequency do-main,” Neurocomputing, vol. 22, pp. 21-34, 1998.MATHCrossRefGoogle Scholar
  8. 8.
    L. Parra and C. Spence, “Convolutive blind separation of nonstationary sources,” IEEE Trans. Speech Audio Processing, vol. 8, no. 3, pp. 320-327, May 2000.CrossRefGoogle Scholar
  9. 9.
    J. Anemüller and B. Kollmeier, “Amplitude modulation decorrelation for con-volutive blind source separation,” in Proc. ICA 2000, June 2000, pp. 215-220.Google Scholar
  10. 10.
    S. Araki, R. Mukai, S. Makino, T. Nishikawa, and H. Saruwatari, “The funda-mental limitation of frequency domain blind source separation for convolutive mixtures of speech,” IEEE Trans. Speech Audio Processing, vol. 11, no. 2, pp. 109-116, 2003.CrossRefGoogle Scholar
  11. 11.
    F. Theis, E. Lang, and C. Puntonet, “A geometric algorithm for overcomplete linear ICA,” Neurocomputing, vol. 56, pp. 381-398, 2004.CrossRefGoogle Scholar
  12. 12.
    P. Bofill and M. Zibulevsky, “Blind separation of more sources than mixtures using sparsity of their short-time Fourier transform,” in Proc. ICA2000, 2000, pp. 87-92.Google Scholar
  13. 13.
    L. Vielva, D. Erdogmus, C. Pantaleon, I. Santamaria, J. Pereda, and J. C. Principe, “Underdetermined blind source separation in a time-varying environ-ment,” in Proc. ICASSP2002, 2002, pp. 3049-3052.Google Scholar
  14. 14.
    P. Bofill, “Underdetermined blind separation of delayed sound sources in the frequency domain,” Neurocomputing, vol. 55, pp. 627-641, 2003.CrossRefGoogle Scholar
  15. 15.
    A. Blin, S. Araki, and S. Makino, “Underdetermined blind separation of convo-lutive mixtures of speech using time-frequency mask and mixing matrix esti-mation,” IEICE Trans. Fundamentals, vol. E88-A, no. 7, pp. 1693-1700, 2005.CrossRefGoogle Scholar
  16. 16.
    S. Winter, W. Kellermann, H. Sawada, and S. Makino, “MAP-based underde-termined blind source separation of convolutive mixtures by hierarchical clus-tering and l1-norm minimization,” EURASIP Journal on Advances in Signal Processing, Article ID 24717, 2007.Google Scholar
  17. 17.
    J. M. Peterson and S. Kadambe, “A probabilistic approach for blind source separation of underdetermined convolutive mixtures,” in Proc. ICASSP 2003, vol. VI, 2003, pp. 581-584.Google Scholar
  18. 18.
    A. Jourjine, S. Rickard, and Ö . Yılmaz, “Blind separation of disjoint orthogonal signals: Demixing N sources from 2 mixtures,” in Proc. ICASSP2000, vol. 12, 2000, pp. 2985-2988.Google Scholar
  19. 19.
    M. Aoki, M. Okamoto, S. Aoki, H. Matsui, T. Sakurai, and Y. Kaneda, “Sound source segregation based on estimating incident angle of each frequency com-ponent of input signals acquired by multiple microphones,” Acoustical Science and Technology, vol. 22, no. 2, pp. 149-157, 2001.CrossRefGoogle Scholar
  20. 20.
    N. Roman, D. Wang, and G. J. Brown, “Speech segregation based on sound localization,” Journal of Acoustical Society of America, vol. 114, no. 4, pp. 2236-2252, Oct. 2003.CrossRefGoogle Scholar
  21. 21.
    S. Rickard, R. Balan, and J. Rosca, “Real-time time-frequency based blind source separation,” in Proc. ICA2001, Dec. 2001, pp. 651-656.Google Scholar
  22. 22.
    R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. Wiley Interscience, 2000.Google Scholar
  23. 23.
    R. Balan, J. Rosca, and S. Rickard, “Non-square blind source separation un-der coherent noise by beamforming and time-frequency masking,” in Proc. ICA2003, Apr. 2003, pp. 313-318.Google Scholar
  24. 24.
    T. Melia, S. Rickard, and C. Fearon, “Histogram-based blind source separa-tion of more sources than sensors using a DUET-ESPRIT technique,” in Proc. EUSIPCO2005, Sept. 2005.Google Scholar
  25. 25.
    S. Araki, S. Makino, H. Sawada, and R. Mukai, “Reducing musical noise by a fine-shift overlap-add method applied to source separation using a time-frequency mask,” in Proc. ICASSP2005, vol. III, Mar. 2005, pp. 81-84.Google Scholar
  26. 26.
    J. Karvanen and A. Cichocki, “Measuring sparseness of noisy signals,” in Proc. ICA2003, Apr. 2003, pp. 125-130.Google Scholar
  27. 27.
    S. Rickard, “Sparse sources are separated sources,” in Proc. EUSIPCO2006, Sept. 2006.Google Scholar
  28. 28.
    S. Rickard and Ö . Yılmaz, “On the approximate W-disjoint orthogonality of speech,” in Proc. ICASSP2002, vol. I, May 2002, pp. 529-532.Google Scholar
  29. 29.
    Ö. Yılmaz and S. Rickard, “Blind separation of speech mixtures via time-frequency masking,” IEEE Trans. Signal Processing, vol. 52, no. 7, pp. 1830-1847, July 2004.CrossRefGoogle Scholar
  30. 30.
    S. Araki, H. Sawada, R. Mukai, and S. Makino, “Underdetermined blind sparse source separation for arbitrarily arranged multiple sensors,” Signal Processing, doi:10.1016/j.sigpro.2007.02.003, 2007.Google Scholar
  31. 31.
    S. Araki, S. Makino, A. Blin, R. Mukai, and H. Sawada, “Underdetermined blind separation for speech in real environments with sparseness and ICA,” in Proc. ICASSP 2004, vol. III, May 2004, pp. 881-884.Google Scholar
  32. 32.
    ——, “A novel blind source separation method with observation vector clus-tering,” in Proc. 2005 International Workshop on Acoustic Echo and Noise Control (IWAENC 2005), Sept. 2005, pp. 117-120.Google Scholar
  33. 33.
  34. 34.
    S. Araki, H. Sawada, R. Mukai, and S. Makino, “DOA estimation for mul-tiple sparse sources with normalized observation vector clustering,” in Proc. ICASSP2006, vol. 5, May 2006, pp. 33-36.Google Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Shoko Araki
    • 1
  • Hiroshi Sawada
    • 1
  • Shoji Makino
    • 1
  1. 1.NTT Communication Science LabsNTT CorporationSoraku-gunJapan

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