Elimination of filtering indeterminacy in blind source separation

Authors:
Kiyotoshi Matsuoka
Affiliations:
Department of Brain Science and Engineering, Kyushu Institute of Technology, Hibikino, Wakamatsu-ku, Kitakyushu 808-0196, Japan
Venue:
Neurocomputing
Year:
2008

Citing 2
Cited 1

Blind separation of sources, Part 1: an adaptive algorithm based on neuromimetic architecture

Signal Processing
Multistage SIMO-Model-Based Blind Source Separation Combining Frequency-Domain ICA and Time-Domain ICA

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

Convolutive blind source separation by efficient blind deconvolution and minimal filter distortion

Neurocomputing

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Abstract

In blind source separation, the definition of sources has an inherent indeterminacy; any linear transform of a source signal can be considered another form of it. Due to this indeterminacy, the designing of a separator has a certain freedom. This paper proposes an idea for eliminating the indeterminacy, i.e., a principle for uniquely normalizing the separator. The output of the separator based on the principle undergoes the least distortion in the demixing process. The method has some good properties: no signal distortion, no fluctuation for nonstationary sources, and simple implementation.