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An Improved Method of Permutation Correction in Convolutive Blind Source Separation

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper proposes an improved method of solving the permutation problem inherent in frequency-domain of convolutive blind source separation (BSS). It combines a novel inter-frequency dependence measure: the power ratio of separated signals, and a simple but effective bin-wise permutation alignment scheme. The proposed method is easy to implement and surpasses the conventional ones. Simulations have shown that it can provide an almost ideal solution of the permutation problem for a case where two or three sources were mixed in a room with a reverberation time of 130 ms.
Rocznik
Strony
493--504
Opis fizyczny
Bibliogr. 16 poz., wykr.
Twórcy
autor
autor
autor
  • Dalian University of Technology School of Electronic and Information Engineering Dalian, 116023, P.R. China, wanglin_2k@sina.com
Bibliografia
  • 1. Allen J.B., Berkley D.A. (1979), Image method for efficiently simulating small room acoustics, Journal of the Acoustical Society of America, 65, 943-950.
  • 2. Bell A.J., Sejnowski T.J. (1995), An information maximization approach to blind separation and blind deconvolution, Neural Computation, 7, 6, 1129-1159.
  • 3. Bingham E., Hyvarien A. (2000), A fast fixed-point algorithm for independent component analysis of complex valued signals, International Journal of Neural Systems, 10, 1, 1-8.
  • 4. Douglas S.C., Gupta M. (2007), Scaled natural gradient algorithms for instantaneous and convolutive blind source separation, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 637-640, Honolulu, USA.
  • 5. Hyvarien A., Karhunen J., Oja E. (2001), Independent Component Analysis, John Wiley & Sons, New York.
  • 6. Ikram M.Z., Morgan D.R. (2000), Exploring permutation inconsistency in blind separation of speechsignals in a reverberant environment, 2000 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1041-1044, Istanbul, Turkey.
  • 7. Ikram M.Z., Morgan D.R. (2005), Permutation inconsistency in blind speech separation: investigation and solutions, IEEE Transactions on Speech and Audio Processing, 13, 1, 1-13.
  • 8. Joho M., Mathis H., Lambert R.H. (2000), Overdetermined blind source separation: Using more sensors than source signals in a noisy mixture, Independent Component Analysis and Blind Signal Separation ICA 2000, pp. 81-86, Helsinki, Finland.
  • 9. Matsuoka K., Nakashima S. (2001), Minimal distortion principle for blind source separation, 2001 International Workshop on Independent Component, pp. 722-727.
  • 10. Murata N., Ikeda S., Ziehe A. (2001), An approach to blind source separation based on temporal structure of speech signals, Neurocomputing, 41, 1-4, 1-24.
  • 11. Pedersen M.S., Larsen J., Kjems U., Parra L.C. (2007), A survey of convolutive blind source separation methods, [in:] Springer handbook on Speech Processing and Speech Communication, 1-34, Springer.
  • 12. Sawada H., Mukai R., Kethulle S., Araki S., Makino S. (2003), Spectral smoothing for frequency-domain blind source separation, 2003 International Workshop on Acoustic Echo and Noise Control, pp. 311-314, Kyoto, Japan.
  • 13. Sawada H., Mukai R., Araki S., Makino S. (2004), A robust and precise method for solving the permutation problem of frequency-domain blind source separation, IEEE Transactions on Speech and Audio Processing, 12, 5, 530-538.
  • 14. Sawada H., Araki S., Makino S. (2007a), Frequency-domain blind source separation, [in:] Blind Speech Separation, 47-78, Springer.
  • 15. Sawada H., Araki S., Makino S. (2007b), Measuring dependence of bin-wise separated signals for permutation alignment in frequency-domain BSS, 2007 IEEE International Symposium on Circuits and Systems, pp. 3247-3250, New Orleans, USA.
  • 16. Smaragdis P. (1998), Blind separation of convolved mixtures in the frequency domain, Neurocomputing, 22, 1-3, 21-34.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS8-0019-0072
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