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Blind Signal SeparationBlind signal separation, a.k.a. blind source separation, is the separation of a set of independent signals from a set of mixed signals, without the aid of information (or with very little information) about the nature of the signals. Blind signal separation relies on the two assumptions: - The (independent) signals are statistically independent. This is reasonable enough; if they were not statistically independent, they wouldn't be independent signals.
- The (independent) signals are not Gaussian white noise. This is in any case a necessary assumption. How can you tell white noise apart from white noise? It's the same: meaningless; random. However, the word signal implies that there is something about the signal which allows it to be distinguished; that there is something to be described by or about the signal, and thus that there is something regular about the signal; i.e. that there is regularity in the signal. Such regularity, being perhaps, a fact of "coming from" a given source, and that source having unique characteristics, thus giving the signal unique characteristics; regularities. These regularities make the signal non-Gaussian, i.e., not completely irregular.
Blind signal separation thus separates a set of signals into a set of signals, such that the regularity of each resulting signal is maximized, and the regularity between the signals is minimized (statistical independence is maximized). Because these temporal redundancies (statistical regularities in the time domain) are "clumped" in this way into the resulting signals, the resulting signals can be more effectively deconvolved than the original signals. There are different methods of blind signal separation: See also: blind deconvolution, Infomax principle, adaptive filtering
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