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Please use this identifier to cite or link to this item: http://hdl.handle.net/10061/7806

Title: Blind Separation of Speech by Fixed-Point ICA with Source Adaptive Negentropy Approximation
Authors: Rajkishore Prasad
Hiroshi Saruwatari
Kiyohiro Shikano
Keywords: blind separation of speech
frequency domain independent component analysis
generalized Gaussian distribution
negentropy maximization
Issue Date: Jul-2005
Publisher: 電子情報通信学会
Journal Title: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Volume: E88-A
Issue: 7
Start page: 1683
End page: 1692
Abstract: 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.
URI: http://hdl.handle.net/10061/7806
URL: https://search.ieice.org/
ISSN: 0916-8508
Rights: Copyright (C) 2005 電子情報通信学会.
Text Version: publisher
Publisher DOI: 10.1093/ietfec/e88-a.7.1683
Appears in Collections:情報科学研究科 / Graduate School of Information Science

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