Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31093
An ICA Algorithm for Separation of Convolutive Mixture of Speech Signals

Authors: Rajkishore Prasad, Hiroshi Saruwatari, Kiyohiro Shikano


This paper describes Independent Component Analysis (ICA) based fixed-point algorithm for the blind separation of the convolutive mixture of speech, picked-up by a linear microphone array. The proposed algorithm extracts independent sources by non- Gaussianizing the Time-Frequency Series of Speech (TFSS) in a deflationary way. The degree of non-Gaussianization is measured by negentropy. The relative performances of algorithm under random initialization and Null beamformer (NBF) based initialization are studied. It has been found that an NBF based initial value gives speedy convergence as well as better separation performance

Keywords: negentropy, blind signal separation, independent component analysis, convolutive mixture

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1467


[1] P. Common, "Independent Component Analysis, A New Concept?," Signal Processing,vol.36, pp.287-314, 1994.
[2] A. Hyvarinen, "Survey on independent component analysis,- Neural Computing Surveys, vol.2, pp.94-128, 1999.
[3] Cardoso J.F., J. Delabrouille, Guillaume, "Independent component analysis of the cosmic microwave background," Proc. ICA2003, 1111- 1116, Nara, Japan, 2003.
[4] Cherry, E. Collin, "Some experiments on recognition of speech, with one and with two ears," Journal of Acoustical Society of America, 25:975-979, 1953.
[5] Cardoso, J.F., "On the performance of orthogonal source separation algorithms," Proc. of EUSIPCO-94, Edinburgh, 1994.
[6] Cardoso, J.F., "Eigenstructure of 4th order cumulants tensor with application to the blind source separation problem," Proc. ICASSP-89, 2109-2112, 1989.
[7] Jutten, C., Herault, J, "Blind separation of sources part 1: An adaptive algorithm based on neuromimetic architechture," J. Signal Processing, 24, 1-10, 1991.
[8] K.Kashino, K. Nakadai, T.Kinoshita, and H.Tanaka, Organization of hierarchical perceptual sounds," Proc. 14th Int. conf. On Artificial Intelligence, vol.1, 158-164,1995.
[9] T.W. Parson, "Separation of speech from interfering speech by means of harmonic selection," J. Acoust. Soc. Am., 60, 911-918, 1976.
[10] M. Unoki, M. Akagai, "A method of signal extraction from noisy signal based on auditory scene analysis," Speech Communication, 27, 261-279, 1999.
[11] O.L. Frost, "An algorithm for linearly constrained adaptive array processing," Proc. IEEE, 60, 926-935, 1972.
[12] L.J. Griffiths, C.W. Jim, "An alternative approach to linearly constrained adaptive beamforming," IEEE Trans. Antennas and Propag., 30, 27-34, 1982.
[13] Y. Kaneda, J.Ohga, Adaptive microphone array system for noise reduction,"IEEE Trans. Acoust., Speech and Signal procs., ASSP-34, 27-34,1986.
[14] H. Saruwatari, S. Kurita, K. Takeda, F. Itakura, T. Nishikawa, and K. Shikano, "Blind source separation combining independent component analysis and beamforming," EURASIP Journal on Applied Signal Processing,Vol.2003, No.11, pp.1135ÔÇö1146, 2003.
[15] T.W.Lee, "Independent Component Analysis", Norway, Kluwer Academic Press, 1998.
[16] S. Haykin, "Unsupervised Adaptive Filtering, John Wiley and Sons Inc.,NewYork, 2000.
[17] P. Samaragadis, Blind separation of convolved mixture in the frequency domain, Neurocomputing, vol.22, pp.21-34, 1998.
[18] Araki, S. et al., "The fundamental relation of frequency domain blind source separation for convolutive mixures of speech,". IEEE Trans. Speech and Audio Processing, Vol. 11, no.2, 109-116, 2003.
[19] S.Ikeda., S. Murata, "A method of ICA in time-frequency domain," Proc. Workshop Indep. Compon. Anal.Signal Sep., 365-367, 1999.
[20] T.Nishikawa, et al., (2003). Blind Source Separation of Acoustic Signals Based on Multistage ICA Combining Frequency-Domain ICA and Time-Domain ICA. IEICE Trans. Fundamentals, Vol.E86-A, pp.846- 58, no.4, April, 2003.
[21] K. Torkkola, "Blind Separation for audio signals-are we there yet? Proc. Workshop on ICA & BSS, France, 1999.
[22] E. Bingham et al., "A fast fixed point algorithm for independent component analysis of complex valued signal," Int. J. of Neural System, 10(1)1: 8, 2000.
[23] Aapo Hyvärinen, "Fast and robust fixed-point algorithms for independent component analysis," IEEE Transactions on Neural Networks 10(3):626-634, 1999.
[24] N. Mitianoudis, N. Davies, "New fixed point solution for convolved audio source separation," Proc. IEEE Workshop on Application of Signal Processing on Audio and Acoustics, New York. (2001).
[25] Cichocki A., S. Amari, "Adaptive Blind Signal and Image Processing, Learning Algorithms and Application," John Wiley & Sons Ltd., 130- 131, 2002.
[26] H. Johnson et al, "Array Signal Processing Concepts and Techniques," Prentice Hall, 1993.
[27] H. Sawada, R. Mukai, S. Araki, S. Makino, A robust approach to the permutation problem of frequency-domain blind source separation," IEEE International Conference on Acoustics, Speech, and Signal (ICASSP2003), 381-384, 2003.
[28] S.Kurita,H.Saruwatari,S.Kajita,K.Takeda, and F. Itakura, "Evaluation of blind signal separation method using directivity pattern under reverberant condition," Proc. ICASSP2000, vol.5, pp.3140-3143, (2000).
[29] Godsill S.J., P.J.W. Rayner., "Digital Audio Restoration," Springer Verlag London, (1998).
[30] Prasad. R. K, H. Saruwatari, K. Shikano, "Problems in blind separation of convolutive speech mixture by negenotropy maximization,", Proc. IWAENC 2003, Kyoto, Japan, 287-290. (2003)