Blind Separation of Speech by Fixed-Point ICA with Source Adaptive Negentropy Approximation

Rajkishore PRASAD  Hiroshi SARUWATARI  Kiyohiro SHIKANO  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E88-A   No.7   pp.1683-1692
Publication Date: 2005/07/01
Online ISSN: 
DOI: 10.1093/ietfec/e88-a.7.1683
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Multi-channel Acoustic Signal Processing)
Category: Blind Source Separation
Keyword: 
blind separation of speech,  frequency domain independent component analysis,  generalized Gaussian distribution,  negentropy maximization,  

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Summary: 
This paper presents a study on the blind separation of a convoluted mixture of speech signals using Frequency Domain Independent Component Analysis (FDICA) algorithm based on the negentropy maximization of Time Frequency Series of Speech (TFSS). The comparative studies on the negentropy approximation of TFSS using generalized Higher Order Statistics (HOS) of different nonquadratic, nonlinear functions are presented. A new nonlinear function based on the statistical modeling of TFSS by exponential power functions has also been proposed. The estimation of standard error and bias, obtained using the sequential delete-one jackknifing method, in the approximation of negentropy of TFSS by different nonlinear functions along with their signal separation performance indicate the superlative power of the exponential-power-based nonlinear function. The proposed nonlinear function has been found to speed-up convergence with slight improvement in the separation quality under reverberant conditions.