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Survey of sparse and non‐sparse methods in source separation
Author(s) -
O'Grady Paul D.,
Pearlmutter Barak A.,
Rickard Scott T.
Publication year - 2005
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.20035
Subject(s) - blind signal separation , computer science , superposition principle , source separation , mixing (physics) , basis (linear algebra) , separation (statistics) , process (computing) , sparse approximation , algorithm , variety (cybernetics) , separation process , pattern recognition (psychology) , artificial intelligence , machine learning , mathematics , telecommunications , physics , mathematical analysis , channel (broadcasting) , geometry , quantum mechanics , chemical engineering , engineering , operating system
Abstract Source separation arises in a variety of signal processing applications, ranging from speech processing to medical image analysis. The separation of a superposition of multiple signals is accomplished by taking into account the structure of the mixing process and by making assumptions about the sources. When the information about the mixing process and sources is limited, the problem is called ‘blind’. By assuming that the sources can be represented sparsely in a given basis, recent research has demonstrated that solutions to previously problematic blind source separation problems can be obtained. In some cases, solutions are possible to problems intractable by previous non‐sparse methods. Indeed, sparse methods provide a powerful approach to the separation of linear mixtures of independent data. This paper surveys the recent arrival of sparse blind source separation methods and the previously existing non‐sparse methods, providing insights and appropriate hooks into theliterature along the way. © 2005 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15;18–33;2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20035