IEICE TRANSACTIONS on Fundamentals
Time-Domain Blind Signal Separation of Convolutive Mixtures via Multidimensional Independent Component Analysis
Summary :
An algorithm for blind signal separation (BSS) of convolutive mixtures is presented. In this algorithm, the BSS problem is treated as multidimensional independent component analysis (ICA) by introducing an extended signal vector which is composed of current and previous samples of signals. It is empirically known that a number of conventional ICA algorithms solve the multidimensional ICA problem up to permutation and scaling of signals. In this paper, we give theoretical justification for using any conventional ICA algorithm. Then, we discuss the remaining problems, i.e., permutation and scaling of signals. To solve the permutation problem, we propose a simple algorithm which classifies the signals obtained by a conventional ICA algorithm into mutually independent subsets by utilizing temporal structure of the signals. For the scaling problem, we prove that the method proposed by Koldovský and Tichavský is theoretically proper in respect of estimating filtered versions of source signals which are observed at sensors.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E92-A No.3 pp.733-744
- Publication Date
- 2009/03/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E92.A.733
- Type of Manuscript
- Special Section PAPER (Special Section on Latest Advances in Fundamental Theories of Signal Processing)
- Category
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Takahiro MURAKAMI, Toshihisa TANAKA, Yoshihisa ISHIDA, "Time-Domain Blind Signal Separation of Convolutive Mixtures via Multidimensional Independent Component Analysis" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 3, pp. 733-744, March 2009, doi: 10.1587/transfun.E92.A.733.
Abstract: An algorithm for blind signal separation (BSS) of convolutive mixtures is presented. In this algorithm, the BSS problem is treated as multidimensional independent component analysis (ICA) by introducing an extended signal vector which is composed of current and previous samples of signals. It is empirically known that a number of conventional ICA algorithms solve the multidimensional ICA problem up to permutation and scaling of signals. In this paper, we give theoretical justification for using any conventional ICA algorithm. Then, we discuss the remaining problems, i.e., permutation and scaling of signals. To solve the permutation problem, we propose a simple algorithm which classifies the signals obtained by a conventional ICA algorithm into mutually independent subsets by utilizing temporal structure of the signals. For the scaling problem, we prove that the method proposed by Koldovský and Tichavský is theoretically proper in respect of estimating filtered versions of source signals which are observed at sensors.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.733/_p
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@ARTICLE{e92-a_3_733,
author={Takahiro MURAKAMI, Toshihisa TANAKA, Yoshihisa ISHIDA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Time-Domain Blind Signal Separation of Convolutive Mixtures via Multidimensional Independent Component Analysis},
year={2009},
volume={E92-A},
number={3},
pages={733-744},
abstract={An algorithm for blind signal separation (BSS) of convolutive mixtures is presented. In this algorithm, the BSS problem is treated as multidimensional independent component analysis (ICA) by introducing an extended signal vector which is composed of current and previous samples of signals. It is empirically known that a number of conventional ICA algorithms solve the multidimensional ICA problem up to permutation and scaling of signals. In this paper, we give theoretical justification for using any conventional ICA algorithm. Then, we discuss the remaining problems, i.e., permutation and scaling of signals. To solve the permutation problem, we propose a simple algorithm which classifies the signals obtained by a conventional ICA algorithm into mutually independent subsets by utilizing temporal structure of the signals. For the scaling problem, we prove that the method proposed by Koldovský and Tichavský is theoretically proper in respect of estimating filtered versions of source signals which are observed at sensors.},
keywords={},
doi={10.1587/transfun.E92.A.733},
ISSN={1745-1337},
month={March},}
Copy
TY - JOUR
TI - Time-Domain Blind Signal Separation of Convolutive Mixtures via Multidimensional Independent Component Analysis
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 733
EP - 744
AU - Takahiro MURAKAMI
AU - Toshihisa TANAKA
AU - Yoshihisa ISHIDA
PY - 2009
DO - 10.1587/transfun.E92.A.733
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2009
AB - An algorithm for blind signal separation (BSS) of convolutive mixtures is presented. In this algorithm, the BSS problem is treated as multidimensional independent component analysis (ICA) by introducing an extended signal vector which is composed of current and previous samples of signals. It is empirically known that a number of conventional ICA algorithms solve the multidimensional ICA problem up to permutation and scaling of signals. In this paper, we give theoretical justification for using any conventional ICA algorithm. Then, we discuss the remaining problems, i.e., permutation and scaling of signals. To solve the permutation problem, we propose a simple algorithm which classifies the signals obtained by a conventional ICA algorithm into mutually independent subsets by utilizing temporal structure of the signals. For the scaling problem, we prove that the method proposed by Koldovský and Tichavský is theoretically proper in respect of estimating filtered versions of source signals which are observed at sensors.
ER -
English
