IEICE TRANSACTIONS on Fundamentals
Blind Separation of Speech by Fixed-Point ICA with Source Adaptive Negentropy Approximation
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E88-A No.7 pp.1683-1692
- Publication Date
- 2005/07/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietfec/e88-a.7.1683
- Type of Manuscript
- Special Section PAPER (Special Section on Multi-channel Acoustic Signal Processing)
- Category
- Blind Source Separation
Authors
Keyword
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Rajkishore PRASAD, Hiroshi SARUWATARI, Kiyohiro SHIKANO, "Blind Separation of Speech by Fixed-Point ICA with Source Adaptive Negentropy Approximation" in IEICE TRANSACTIONS on Fundamentals,
vol. E88-A, no. 7, pp. 1683-1692, July 2005, doi: 10.1093/ietfec/e88-a.7.1683.
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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e88-a.7.1683/_p
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@ARTICLE{e88-a_7_1683,
author={Rajkishore PRASAD, Hiroshi SARUWATARI, Kiyohiro SHIKANO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Blind Separation of Speech by Fixed-Point ICA with Source Adaptive Negentropy Approximation},
year={2005},
volume={E88-A},
number={7},
pages={1683-1692},
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.},
keywords={},
doi={10.1093/ietfec/e88-a.7.1683},
ISSN={},
month={July},}
Copy
TY - JOUR
TI - Blind Separation of Speech by Fixed-Point ICA with Source Adaptive Negentropy Approximation
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1683
EP - 1692
AU - Rajkishore PRASAD
AU - Hiroshi SARUWATARI
AU - Kiyohiro SHIKANO
PY - 2005
DO - 10.1093/ietfec/e88-a.7.1683
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E88-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2005
AB - 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.
ER -
English
