Work

Convexity in Source Separation : Models, geometry, and algorithms

Year: 2014

Type: article

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.

Source: IEEE Signal Processing Magazine

DOI: 10.1109/msp.2013.2296605

Authors: Michael B. McCoy, Volkan Cevher, Quoc Tran Dinh, Afsaneh Asaei, Luca Baldassarre

Institutions: California Institute of Technology, École Polytechnique Fédérale de Lausanne, Idiap Research Institute

Language: English

Topic: Blind Source Separation Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2143001084

FWCI: 11.62

Cites: 37

Cited by: 45

Related to: 10