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Mandatory Fields
Reviews
O'Grady, PD;Pearlmutter, BA;Rickard, ST
2005
January
Survey of sparse and non-sparse methods in source separation
Published
1
91 ()
Optional Fields
BLIND SOURCE SEPARATION INDEPENDENT COMPONENT ANALYSIS NONNEGATIVE MATRIX FACTORIZATION OVERCOMPLETE REPRESENTATIONS FREQUENCY-DOMAIN MIXTURES DECONVOLUTION DECOMPOSITION ALGORITHMS PRINCIPLES
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 nonsparse methods, providing insights and appropriate hooks into the-literature along the way. (c) 2005 Wiley Periodicals, Inc.
HOBOKEN
JOHN WILEY & SONS INC
0899-9457
18
33
Grant Details