IEICE TRANSACTIONS on Fundamentals
Fast-Convergence Algorithm for Blind Source Separation Based on Array Signal Processing
Summary :
We propose a new algorithm for blind source separation (BSS), in which independent component analysis (ICA) and beamforming are combined to resolve the low-convergence problem through optimization in ICA. The proposed method consists of the following two parts: frequency-domain ICA with direction-of-arrival (DOA) estimation, and null beamforming based on the estimated DOA. The alternation of learning between ICA and beamforming can realize fast- and high-convergence optimization. The results of the signal separation experiments reveal that the signal separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E86-A No.3 pp.634-639
- Publication Date
- 2003/03/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section LETTER (Special Section on Blind Signal Processing: Independent Component Analysis and Signal Separation)
- Category
- Convolutive Systems
Authors
Keyword
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Hiroshi SARUWATARI, Toshiya KAWAMURA, Tsuyoki NISHIKAWA, Kiyohiro SHIKANO, "Fast-Convergence Algorithm for Blind Source Separation Based on Array Signal Processing" in IEICE TRANSACTIONS on Fundamentals,
vol. E86-A, no. 3, pp. 634-639, March 2003, doi: .
Abstract: We propose a new algorithm for blind source separation (BSS), in which independent component analysis (ICA) and beamforming are combined to resolve the low-convergence problem through optimization in ICA. The proposed method consists of the following two parts: frequency-domain ICA with direction-of-arrival (DOA) estimation, and null beamforming based on the estimated DOA. The alternation of learning between ICA and beamforming can realize fast- and high-convergence optimization. The results of the signal separation experiments reveal that the signal separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e86-a_3_634/_p
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@ARTICLE{e86-a_3_634,
author={Hiroshi SARUWATARI, Toshiya KAWAMURA, Tsuyoki NISHIKAWA, Kiyohiro SHIKANO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Fast-Convergence Algorithm for Blind Source Separation Based on Array Signal Processing},
year={2003},
volume={E86-A},
number={3},
pages={634-639},
abstract={We propose a new algorithm for blind source separation (BSS), in which independent component analysis (ICA) and beamforming are combined to resolve the low-convergence problem through optimization in ICA. The proposed method consists of the following two parts: frequency-domain ICA with direction-of-arrival (DOA) estimation, and null beamforming based on the estimated DOA. The alternation of learning between ICA and beamforming can realize fast- and high-convergence optimization. The results of the signal separation experiments reveal that the signal separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method.},
keywords={},
doi={},
ISSN={},
month={March},}
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TY - JOUR
TI - Fast-Convergence Algorithm for Blind Source Separation Based on Array Signal Processing
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 634
EP - 639
AU - Hiroshi SARUWATARI
AU - Toshiya KAWAMURA
AU - Tsuyoki NISHIKAWA
AU - Kiyohiro SHIKANO
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E86-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2003
AB - We propose a new algorithm for blind source separation (BSS), in which independent component analysis (ICA) and beamforming are combined to resolve the low-convergence problem through optimization in ICA. The proposed method consists of the following two parts: frequency-domain ICA with direction-of-arrival (DOA) estimation, and null beamforming based on the estimated DOA. The alternation of learning between ICA and beamforming can realize fast- and high-convergence optimization. The results of the signal separation experiments reveal that the signal separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method.
ER -
English
