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Equivariant nonstationary source separation.

Seungjin Choi·Andrzej Cichocki·Shunichi Amari
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Abstract

Most of source separation methods focus on stationary sources, so higher-order statistics is necessary for successful separation, unless sources are temporally correlated. For nonstationary sources, however, it was shown [Neural Networks 8 (1995) 411] that source separation could be achieved by second-order decorrelation. In this paper, we consider the cost function proposed by Matsuoka et al. [Neural Networks 8 (1995) 411] and derive natural gradient learning algorithms for both fully connected recurrent network and feedforward network. Since our algorithms employ the natural gradient method, they possess the equivariant property and find a steepest descent direction unlike the algorithm [Neural Networks 8 (1995) 411]. We also show that our algorithms are always locally stable, regardless of probability distributions of nonstationary sources.

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AlgorithmsComputer SimulationNeural Networks, ComputerJournal ArticleResearch Support, Non-U.S. Gov't

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Seungjin Choi, Andrzej Cichocki, Shunichi Amari. 2002. Equivariant nonstationary source separation.. https://doi.org/10.1016/s0893-6080(01)00137-x

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