Blind signal separation
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Blind signal separation, a.k.a. blind source separation, is the separation of a set of 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 following assumption:
- The source signals are non-redundant. For example, the signals may be mutually statistically independent or decorrelated. Other assumptions of non-redundancy may be also assumed.
Blind signal separation thus separates a set of signals into a set of other signals, such that the regularity of each resulting signal is maximized, and the regularity between the signals is minimized (i.e., statistical independence is maximized or decorrelation is minimized).
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:
- Principal components analysis
- Singular value decomposition
- Independent component analysis
- Dependent component analysis
- Non-negative matrix factorization
External links
See also: deconvolution, Infomax principle, adaptive filtering