Survey of sparse and non-sparse methods in source separation


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 the literature along the way.

DOI: 10.1002/ima.20035

Extracted Key Phrases

9 Figures and Tables


Citations per Year

147 Citations

Semantic Scholar estimates that this publication has 147 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{OGrady2005SurveyOS, title={Survey of sparse and non-sparse methods in source separation}, author={Paul D. O'Grady and Barak A. Pearlmutter and Scott T. Rickard}, journal={Int. J. Imaging Systems and Technology}, year={2005}, volume={15}, pages={18-33} }