Time-Domain Blind Signal Separation of Convolutive Mixtures via Multidimensional Independent Component Analysis
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概要
- 論文の詳細を見る
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.
- （社）電子情報通信学会の論文
- 2009-03-01
著者
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TANAKA TOSHIHISA
Department of Clinical Neuroscience & Psychiatry, Osaka University Graduate School of Medicine
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Tanaka Toshihisa
Tokyo Univ. Agriculture And Technol. Koganei‐shi Jpn
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Murakami Takahiro
Department Of Electrical And Electronic Engineering Tokyo University Of Agriculture And Technology (
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Ishida Yoshihisa
Department Of Electronic Engineering Osaka Institute Of Technology Omiya
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Murakami Takahiro
Department Of Applied Chemistry School Of Engineering Tokai University
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Ishida Yoshihisa
Dep. Of Electronics And Bioinformatics Meiji Univ.
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