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Blind signal separation (BSS), also known as 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 source signals or the mixing process.
Blind signal separation relies on the assumption that the source signals do not correlate with each other. For example, the signals may be statistically independent or decorrelated. 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).
Typical methods of blind signal separation include
There are multiple potential applications of blind signal separation. In acoustics different sound sources are recorded simultaneously with possibly multiple microphones. These sources may be speech or music, or underwater signals recorded in passive sonar. In radio communications, antenna arrays receive mixtures of different communication signals. Source separation has also been applied to image processing. Finally, is has been used to in biomedical signals1 like electrocardiogram (EKG/ECG) and electromyogram (EMG) and other bio-potentials.
To learn more about various applications of blind signal separation, check out the following links: