Time-Domain Blind Signal Separation of Convolutive Mixtures via Multidimensional Independent Component Analysis
Time-Domain Blind Signal Separation of Convolutive Mixtures via Multidimensional Independent Component Analysis
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DOI：
10.1587/transfun.e92.a.733
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发表时间：
2009-03
期刊：
影响因子：
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通讯作者：
T. Murakami;Toshihisa Tanaka;Y. Ishida
中科院分区：
文献类型：
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作者：
T. Murakami;Toshihisa Tanaka;Y. Ishida
提出了一种卷积混合盲信号分离（BSS）算法。在该算法中，通过引入由当前和先前信号样本组成的扩展信号向量，将BSS问题视为多维独立分量分析（ICA）。根据经验可知，许多传统的 ICA 算法可以解决多维 ICA 问题，直至信号的排列和缩放。在本文中，我们给出了使用任何传统 ICA 算法的理论依据。然后，我们讨论剩下的问题，即信号的排列和缩放。为了解决排列问题，我们提出了一种简单的算法，利用信号的时间结构将传统 ICA 算法获得的信号分类为相互独立的子集。对于缩放问题，我们证明 Koldovský 和 Tichavský 提出的方法在估计传感器处观察到的源信号的滤波版本方面理论上是正确的。
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.