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Source separation using single channel ICA
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DOI：10.1016/j.sigpro.2007.01.011.png)
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Many researchers have recently used independent component analysis (ICA) to generate codebooks or features for a single channel of data. We examine the nature of these codebooks and identify when such features can be used to extract independent components from a stationary scalar time series. This question is motivated by empirical work that suggests that single channel ICA can sometimes be used to separate out important components from a time series. Here we show that as long as the sources are reasonably spectrally disjoint then we can identify and approximately separate out individual sources. However, the linear nature of the separation equations means that when the sources have substantially overlapping spectra both identification using standard ICA and linear separation are no longer possible. (C) 2007 Elsevier B.V. All rights reserved.
Keyword:
independent component analysis
blind source separation
sparse coding
single channel ICA
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