| Revision as of 16:27, 26 August 2016 editDatGuy (talk | contribs)Edit filter managers, Autopatrolled, Checkusers, Administrators39,858 edits Reverted 1 good faith edit by Shahinmabdulla using STiki← Previous edit | Revision as of 05:32, 4 November 2016 edit undoInternetArchiveBot (talk | contribs)Bots, Pending changes reviewers5,388,432 edits Rescuing 1 sources and tagging 0 as dead. #IABot (v1.2.6)Next edit → | ||
| Line 28: | Line 28: | ||
| == External links == | == External links == | ||
| {{Commons category|Blind signal separation}} | {{Commons category|Blind signal separation}} | ||
| * | * | ||
| * | * | ||
Revision as of 05:32, 4 November 2016
Blind signal separation, also known as blind source separation, is the separation of a set of source 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. This problem is in general highly underdetermined, but useful solutions can be derived under a surprising variety of conditions. Much of the early literature in this field focuses on the separation of temporal signals such as audio. However, blind signal separation is now routinely performed on multidimensional data, such as images and tensors, which may involve no time dimension whatsoever.
Approaches
Since the chief difficulty of the problem is its underdetermination, methods for blind source separation generally seek to narrow the set of possible solutions in a way that is unlikely to exclude the desired solution. In one approach, exemplified by principal and independent component analysis, one seeks source signals that are minimally correlated or maximally independent in a probabilistic or information-theoretic sense. A second approach, exemplified by nonnegative matrix factorization, is to impose structural constraints on the source signals. These structural constraints may be derived from a generative model of the signal, but are more commonly heuristics justified by good empirical performance. A common theme in the second approach is to impose some kind of low-complexity constraint on the signal, such as sparsity in some basis for the signal space. This approach can be particularly effective if one requires not the whole signal, but merely its most salient features.
Methods
There are different methods of blind signal separation:
- Principal components analysis
- Singular value decomposition
- Independent component analysis
- Dependent component analysis
- Non-negative matrix factorization
- Low-complexity coding and decoding
- Stationary subspace analysis
- Common spatial pattern
See also
References
- Ranjan Acharyya (editors) (2008): A New Approach for Blind Source Separation of Convolutive Sources, ISBN 3-639-07797-0 ISBN 978-3639077971
External links
- Blind source separation flash presentation
- Removing electroencephalographic artifacts by blind source separation
 | This signal processing-related article is a stub. You can help Misplaced Pages by expanding it. |