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.
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References
S. Haykin, Ed., Unsupervised Adaptive Filtering (Volume I: Blind Source Sep-aration). John Wiley & Sons, 2000.
A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis. John Wiley & Sons, 2001.
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.
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.
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.
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.
P. Smaragdis, “Blind separation of convolved mixtures in the frequency do-main,” Neurocomputing, vol. 22, pp. 21-34, 1998.
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.
J. Anemüller and B. Kollmeier, “Amplitude modulation decorrelation for con-volutive blind source separation,” in Proc. ICA 2000, June 2000, pp. 215-220.
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.
F. Theis, E. Lang, and C. Puntonet, “A geometric algorithm for overcomplete linear ICA,” Neurocomputing, vol. 56, pp. 381-398, 2004.
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.
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.
P. Bofill, “Underdetermined blind separation of delayed sound sources in the frequency domain,” Neurocomputing, vol. 55, pp. 627-641, 2003.
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.
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.
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.
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.
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.
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.
S. Rickard, R. Balan, and J. Rosca, “Real-time time-frequency based blind source separation,” in Proc. ICA2001, Dec. 2001, pp. 651-656.
R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. Wiley Interscience, 2000.
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.
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.
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.
J. Karvanen and A. Cichocki, “Measuring sparseness of noisy signals,” in Proc. ICA2003, Apr. 2003, pp. 125-130.
S. Rickard, “Sparse sources are separated sources,” in Proc. EUSIPCO2006, Sept. 2006.
S. Rickard and Ö . Yılmaz, “On the approximate W-disjoint orthogonality of speech,” in Proc. ICASSP2002, vol. I, May 2002, pp. 529-532.
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.
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.
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.
——, “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.
“http://www.kecl.ntt.co.jp/icl/signal/araki/xcluster fine.html.”
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.
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Araki, S., Sawada, H., Makino, S. (2007). K-means Based Underdetermined Blind Speech Separation. In: Makino, S., Sawada, H., Lee, TW. (eds) Blind Speech Separation. Signals and Communication Technology. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6479-1_9
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