Blind source separation exploiting higher-order frequency dependencies

IEEE Transactions on Audio, Speech and Language Processing

Volumn 15, Issue 1, 2007, Pages 70-79

  Kim, Taesu ^a,b   Attias, Hagai T ^c   Lee, Soo Young ^a   Lee, Te Won ^b  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^b UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^c Golden Metallic Inc   (United States)

Author keywords

Blind source separation (BSS); Cocktail party problem; Convolutive mixture; Frequency domain; Higher order dependency; Independent component analysis; Permutation problem

Indexed keywords

COCKTAIL PARTY PROBLEM; CONVOLUTIVE MIXTURE; FREQUENCY DOMAIN; HIGHER ORDER DEPENDENCY; PERMUTATION PROBLEM;

APARTMENT HOUSES; BINS; ELECTROACOUSTIC TRANSDUCERS; FEATURE EXTRACTION; FREQUENCY DOMAIN ANALYSIS; INDEPENDENT COMPONENT ANALYSIS; LEARNING ALGORITHMS; LEARNING SYSTEMS; RANDOM VARIABLES; SEPARATION; SIGNAL ANALYSIS; SPEECH ANALYSIS;

BLIND SOURCE SEPARATION;

EID: 34247155553     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2006.872618     Document Type: Article

References (28)

1. T.-W. Lee, Independent Component Analysis: Theory and Applications. Boston, MA: Kluwer, 1998.
2. S. Roberts and R. Everson, Independent Component Analysis: Principles and Practice. Cambridge, U.K.: Cambridge Univ. Press, 2001.
3. A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis. New York: Wiley Interscience, 2001.
4. D. Yellin and E. Weinstein, "Multichannel signal separation: methods and analysis," IEEE Trans. Signal Process., vol. 44, no. 1, pp. 106-118, Jan. 1996.
5. R. Lambert, "Multichannel blind deconvolution: FIR matrix algebra and separation of multipath mixtures," Ph.D. dissertation, Univ. Southern California, Los Angeles, 1996.
6. K. Torkkola, "Blind separation of convolved sources based on information maximization," in Proc. IEEE Int. Workshop Neural Netw. Signal Process., 1996, pp. 423-432.
7. T.-W. Lee, A. J. Bell, and R. Lambert, "Blind separation of convolved and delayed sources," Adv. Neural Inf. Process. Syst., pp. 758-764, 1997.
8. S. Weiß, "On adaptive filtering on oversampled subbands," Ph.D. dissertation, Signal Process. Div., Univ. Strathclyde, Glasgow, U.K., 1997.
9. P. Smaragdis, "Blind separation of convolved mixtures in the frequency domain," Neurocomput., vol. 22, pp. 21-34, 1998.
10. L. Parra and C. Spence, "Convolutive blind separation of nonstationary sources," IEEE Trans. Speech Audio Process., vol. 8, no. 3, pp. 320-327, May 2000.
11. F. Asano, S. Ikeda, M. Ogawa, H. Asoh, and N. Kitawaki, "A combined approach of array processing and independent component analysis for blind separation of acoustic signals," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2001, pp. 2729-2732.
12. S. Kurita, H. Saruwatari, S. Kajita, K. Takeda, and F. Itakura, "Evaluation of blind signal separation method using directivity pattern under reverberant conditions," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2000, pp. 3140-3143.
13. M. Z. Ikram and D. R. Morgan, "A beamforming approach to permutation alignment for multichannel frequency-domain blind speech separation," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2002, pp. 881-884.
14. H. Sawada, R. Mukai, S. Araki, and S. Makino, "A robust and precise method for solving the permutation problem of frequency-domain blind source separation," IEEE Trans. Speech Audio Process., vol. 12, no. 5, p. 530, Sep. 2004.
15. J. Anemüller and B. Kollmeier, "Amplitude modulation decorrelation for convolutive blind source separation," in Proc. Int. Conf. Independent Compon. Anal. Blind Source Separation, 2000, pp. 215-220.
16. N. Murata, S. Ikeda, and A. Ziehe, "An approach to blind source separation based on temporal structure of speech signals," Neurocomput., vol. 41, pp. 1-24, 2001.
17. S.-I. Amari, A. Cichocki, and H. H. Yang, "A new learning algorithm for blind signal separation," Adv. Neural Inf. Process. Syst., vol. 8, pp. 752-763, 1996.
18. K. Matsuoka and S. Nakashima, "Minimal distortion principle for blind source separation," in Proc. Int. Conf. Independent Compon. Anal. Blind Source Separation, 2001, pp. 722-727.
19. R. B. Stephens and A. E. Bate, Acoustics and Vibrational Physics. London, U.K.: E. Arnold, 1966.
20. J. B. Allen and D. A. Berkley, "Image method for efficiently simulating small room acoustics," J. Acoust. Soc. Amer., vol. 65, pp. 943-950, 1979.
21. W. G. Gardner, "The Virtual Acoustic Room," Master's thesis, Massachusetts Inst. Technol., Cambridge, 1992.
22. J.-F. Cardoso, "Multidimensional independent component analysis," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 1998, pp. 1941-1944.
23. A. Hyvärinen and P. O. Hoyer, "Emergence of phase and shift invariant features by decomposition of natural images into independent feature subspaces," Neural Comput., vol. 12, no. 7, pp. 1705-1720, 2000.
24. A. Hyvärinen, P. O. Hoyer, and M. Inki, "Topographic independent component analysis," Neural Comput., vol. 13, no. 7, pp. 1527-1558, 2001.
25. T.-W. Lee, M. S. Lewicki, and T. Sejnowski, "ICA mixture models for unsupervised classification of non-Gaussian classes and automatic context switching in blind separation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 10, pp. 1078-1089, Oct. 2000.
26. T.-W. Lee and M. S. Lewicki, "Unsupervised image classification, segmentation, and enhancement using ICA mixture models," IEEE Trans. Image Process., vol. 11, no. 3, pp. 270-279, Mar. 2002.
27. Y. Karklin and M. S. Lewicki, "Learning higher order structures in natural images," Netw.: Comput. Neural Syst., vol. 14, no. 3, pp. 483-499, 2003.
28. H.-J. Park and T.-W. Lee, "Modeling nonlinear dependencies in natural images using mixture of Laplacian distribution," Adv. Neural Inf. Process. Syst., pp. 1041-1048, 2005.