O'Grady, Paul D. and Pearlmutter, Barak A. and Rickard, Scott T. (2005) Survey of Sparse and Non-Sparse Methods in Source Separation. International Journal of Imaging Systems and Technology, 15 (1). pp. 18-33. ISSN 0899-9457

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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 nonsparse methods, providing insights and appropriate hooks into theliterature along the way.

Item Type:	Article
Keywords:	Blind Sources Separation; sparse methods; Nonnegative Matrix Factorization;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	5502
Identification Number:	https://doi.org/10.1002/ima.20035
Depositing User:	Barak Pearlmutter
Date Deposited:	15 Oct 2014 11:02
Journal or Publication Title:	International Journal of Imaging Systems and Technology
Publisher:	Wiley Periodicals
Refereed:	Yes
Funders:	Science Foundation Ireland grant 00/PI.1/C067
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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