Convexity in source separation: Models, geometry, and algorithms

Source separation, or demixing, is the process of extracting multiple components entangled within a signal. Contemporary signal processing presents a host of difficult source separation problems, from interference cancellation to background subtraction, blind deconvolution, and even dictionary learning. Despite the recent progress in each of these applications, advances in high-throughput sensor technology place demixing algorithms under pressure to accommodate extremely high-dimensional signals, separate an ever larger number of sources, and cope with more sophisticated signal and mixing models. These difficulties are exacerbated by the need for real-time action in automated decision-making systems.


Published in:
Signal Processing Magazine, IEEE, 31, 3, 87-95
Year:
2013
Laboratories:




 Record created 2013-12-19, last modified 2018-03-18

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06784106 - Download fulltextPDF
McCoy_SPM_2013 - Download fulltextPDF
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