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Published May 2014 | Submitted
Journal Article Open

Convexity in Source Separation: Models, geometry, and algorithms

Abstract

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.

Additional Information

© 2014 IEEE. Date of Current Version: 07 April 2014. The authors thank Joel A. Tropp for his helpful and detailed comments on this work. MBM is supported by ONR awards N00014-08-1-0883 and N00014-11-1002, AFOSR award FA9550-09-1-064. Work of VC, QTD, and LB is supported in part by the European Commission under Grant MIRG-268398, ERC Future Proof, SNF 200021-132548, SNF 200021-146750 and SNF CRSII2-147633. The work of AA is funded by SNF NCCR IM2.

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August 20, 2023
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