An ICA Algorithm for Separation of Convolutive Mixture of Speech Signals

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

• Rajkishore Prasad
• Hiroshi Saruwatari
• Kiyohiro Shikano

• Blind signal separation
• independent component analysis
• negentropy
• convolutive mixture.

[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)