This work examines a semi-blind source separation problem having applications in audio, image, and video processing. The essential aim is to separate one source whose local structure is partially or approximately known from another a priori unspecified but structured source, given only a single linear combination of the two sources. We propose a novel separation technique based on local sparse approximations. A key feature of our procedure is the online learning of dictionaries (using only the data itself) to sparsely model the unknown structured background source. The performance of the proposed approach is demonstrated via simulation in a stylized application of audio source separation.
University of Minnesota M.S. thesis. Major: Electrical Engineering. Advisor: Prof. Jarvis Haupt. 1 computer file (PDF); vii, 43 pages, appendix A.
Semi-blind source separation via sparse representations and online dictionary learning.
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