Abstract:
Source separation, or demixing, is the process of extracting multiple components entangled within a signal. Contemporary signal processing presents a host of difficult so...Show MoreMetadata
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.
Published in: IEEE Signal Processing Magazine ( Volume: 31, Issue: 3, May 2014)
California Institute of Technology, Pasadena, CA, US
Michael B. McCoy (mccoy@caltech.edu) received the B.S. degree in electrical engineering (honors) in 2007 from the University of Texas at Austin and the Ph.D. degree in applied and computational mathematics in 2013 from the California Institute of Technology (Caltech), Pasadena. His thesis focused on convex methods for signal decompositions and earned a WP Carey & Co. Inc. Prize for an outstanding doctoral dissertation. He...Show More
Michael B. McCoy (mccoy@caltech.edu) received the B.S. degree in electrical engineering (honors) in 2007 from the University of Texas at Austin and the Ph.D. degree in applied and computational mathematics in 2013 from the California Institute of Technology (Caltech), Pasadena. His thesis focused on convex methods for signal decompositions and earned a WP Carey & Co. Inc. Prize for an outstanding doctoral dissertation. He...View more
Ecole Polytechnique Federale de Lausanne, Lausanne, VD, CH
Volkan Cevher (volkan.cevher@epfl.ch) received the B.S. (valedictorian) degree in electrical engineering in 1999 from Bilkent University in Ankara, Turkey, and the Ph.D. degree in electrical and computer engineering in 2005 from the Georgia Institute of Technology in Atlanta. He held research scientist positions at the University of Maryland, College Park, from 2006 to 2007 and at Rice University in Houston, Texas, from 2...Show More
Volkan Cevher (volkan.cevher@epfl.ch) received the B.S. (valedictorian) degree in electrical engineering in 1999 from Bilkent University in Ankara, Turkey, and the Ph.D. degree in electrical and computer engineering in 2005 from the Georgia Institute of Technology in Atlanta. He held research scientist positions at the University of Maryland, College Park, from 2006 to 2007 and at Rice University in Houston, Texas, from 2...View more
Ecole Polytechnique Federale de Lausanne, Lausanne, VD, CH
Quoc Tran Dinh (quoc.tranDinh@epfl.ch) received the B.S. degree in applied mathematics and informatics and the M.S. degree in computer science, both from Vietnam National University, Hanoi, in 2001 and 2004, respectively, and the Ph.D. degree in electrical engineering from the Department of Electrical Engineering and Optimization in Engineering Center, KU Leuven, Belgium. He is currently a postdoctoral researcher with the...Show More
Quoc Tran Dinh (quoc.tranDinh@epfl.ch) received the B.S. degree in applied mathematics and informatics and the M.S. degree in computer science, both from Vietnam National University, Hanoi, in 2001 and 2004, respectively, and the Ph.D. degree in electrical engineering from the Department of Electrical Engineering and Optimization in Engineering Center, KU Leuven, Belgium. He is currently a postdoctoral researcher with the...View more
Idiap Research Institute, Martigny, Valais, CH
Afsaneh Asaei (afsaneh.asaei@idiap.ch) received the B.S. degree from Amirkabir University of Technology and the M.S. (honors) degree from Sharif University of Technology, in electrical and computer engineering, respectively. She held a research engineer position at Iran Telecommunication Research Center (ITRC) from 2002 to 2008. She then joined Idiap Research Institute in Martigny, Switzerland, and was a Marie Curie fello...Show More
Afsaneh Asaei (afsaneh.asaei@idiap.ch) received the B.S. degree from Amirkabir University of Technology and the M.S. (honors) degree from Sharif University of Technology, in electrical and computer engineering, respectively. She held a research engineer position at Iran Telecommunication Research Center (ITRC) from 2002 to 2008. She then joined Idiap Research Institute in Martigny, Switzerland, and was a Marie Curie fello...View more
Ecole Polytechnique Federale de Lausanne, Lausanne, VD, CH
Luca Baldassarre (luca.baldassarre@epfl.ch) received the M.S. degree in physics in 2006 and the Ph.D. degree in machine learning in 2010 from the University of Genoa, Italy. He then joined the Computer Science Department of University College London, United Kingdom, to work with Prof. Massimiliano Pontil on structured sparsity models for machine learning and convex optimization. Currently he is with the Laboratory for Inf...Show More
Luca Baldassarre (luca.baldassarre@epfl.ch) received the M.S. degree in physics in 2006 and the Ph.D. degree in machine learning in 2010 from the University of Genoa, Italy. He then joined the Computer Science Department of University College London, United Kingdom, to work with Prof. Massimiliano Pontil on structured sparsity models for machine learning and convex optimization. Currently he is with the Laboratory for Inf...View more
Contents California Institute of Technology, Pasadena, CA, US
Michael B. McCoy (mccoy@caltech.edu) received the B.S. degree in electrical engineering (honors) in 2007 from the University of Texas at Austin and the Ph.D. degree in applied and computational mathematics in 2013 from the California Institute of Technology (Caltech), Pasadena. His thesis focused on convex methods for signal decompositions and earned a WP Carey & Co. Inc. Prize for an outstanding doctoral dissertation. He is currently a postdoctoral scholar at Caltech, where his research explores the intersections of optimization, signal processing, statistics, and geometry.
Michael B. McCoy (mccoy@caltech.edu) received the B.S. degree in electrical engineering (honors) in 2007 from the University of Texas at Austin and the Ph.D. degree in applied and computational mathematics in 2013 from the California Institute of Technology (Caltech), Pasadena. His thesis focused on convex methods for signal decompositions and earned a WP Carey & Co. Inc. Prize for an outstanding doctoral dissertation. He is currently a postdoctoral scholar at Caltech, where his research explores the intersections of optimization, signal processing, statistics, and geometry.View more
Ecole Polytechnique Federale de Lausanne, Lausanne, VD, CH
Volkan Cevher (volkan.cevher@epfl.ch) received the B.S. (valedictorian) degree in electrical engineering in 1999 from Bilkent University in Ankara, Turkey, and the Ph.D. degree in electrical and computer engineering in 2005 from the Georgia Institute of Technology in Atlanta. He held research scientist positions at the University of Maryland, College Park, from 2006 to 2007 and at Rice University in Houston, Texas, from 2008 to 2009. Currently, he is an assistant professor at the Swiss Federal Institute of Technology Lausanne and a faculty fellow in the Electrical and Computer Engineering Department at Rice University. His research interests include signal processing theory, machine learning, graphical models, and information theory. He received a Best Paper Award at the Signal Processing with Adaptive Sparse Representations Workshop in 2009 and a European Research Council Starting Grant in 2011.
Volkan Cevher (volkan.cevher@epfl.ch) received the B.S. (valedictorian) degree in electrical engineering in 1999 from Bilkent University in Ankara, Turkey, and the Ph.D. degree in electrical and computer engineering in 2005 from the Georgia Institute of Technology in Atlanta. He held research scientist positions at the University of Maryland, College Park, from 2006 to 2007 and at Rice University in Houston, Texas, from 2008 to 2009. Currently, he is an assistant professor at the Swiss Federal Institute of Technology Lausanne and a faculty fellow in the Electrical and Computer Engineering Department at Rice University. His research interests include signal processing theory, machine learning, graphical models, and information theory. He received a Best Paper Award at the Signal Processing with Adaptive Sparse Representations Workshop in 2009 and a European Research Council Starting Grant in 2011.View more
Ecole Polytechnique Federale de Lausanne, Lausanne, VD, CH
Quoc Tran Dinh (quoc.tranDinh@epfl.ch) received the B.S. degree in applied mathematics and informatics and the M.S. degree in computer science, both from Vietnam National University, Hanoi, in 2001 and 2004, respectively, and the Ph.D. degree in electrical engineering from the Department of Electrical Engineering and Optimization in Engineering Center, KU Leuven, Belgium. He is currently a postdoctoral researcher with the Laboratory for Information and Inference Systems, Ecole Polytechnique Federale de Lausanne, Switzerland. His research interests include methods for convex optimization, sequential convex programming, parametric optimization, optimization in machine learning, and methods for variational inequalities and equilibrium problems.
Quoc Tran Dinh (quoc.tranDinh@epfl.ch) received the B.S. degree in applied mathematics and informatics and the M.S. degree in computer science, both from Vietnam National University, Hanoi, in 2001 and 2004, respectively, and the Ph.D. degree in electrical engineering from the Department of Electrical Engineering and Optimization in Engineering Center, KU Leuven, Belgium. He is currently a postdoctoral researcher with the Laboratory for Information and Inference Systems, Ecole Polytechnique Federale de Lausanne, Switzerland. His research interests include methods for convex optimization, sequential convex programming, parametric optimization, optimization in machine learning, and methods for variational inequalities and equilibrium problems.View more
Idiap Research Institute, Martigny, Valais, CH
Afsaneh Asaei (afsaneh.asaei@idiap.ch) received the B.S. degree from Amirkabir University of Technology and the M.S. (honors) degree from Sharif University of Technology, in electrical and computer engineering, respectively. She held a research engineer position at Iran Telecommunication Research Center (ITRC) from 2002 to 2008. She then joined Idiap Research Institute in Martigny, Switzerland, and was a Marie Curie fellow on speech communication with adaptive learning training network. She received the Ph.D. degree in 2013 from Ecole Polytechnique Federale de Lausanne. Her thesis focused on model-based sparsity for reverberant speech processing, and its key idea was awarded the IEEE Spoken Language Processing Grant. Currently, she is a research scientist at Idiap Research Institute. Her research interests lie in the areas of signal processing, machine learning, statistics, acoustics, auditory scene analysis and cognition, and sparse signal recovery and acquisition.
Afsaneh Asaei (afsaneh.asaei@idiap.ch) received the B.S. degree from Amirkabir University of Technology and the M.S. (honors) degree from Sharif University of Technology, in electrical and computer engineering, respectively. She held a research engineer position at Iran Telecommunication Research Center (ITRC) from 2002 to 2008. She then joined Idiap Research Institute in Martigny, Switzerland, and was a Marie Curie fellow on speech communication with adaptive learning training network. She received the Ph.D. degree in 2013 from Ecole Polytechnique Federale de Lausanne. Her thesis focused on model-based sparsity for reverberant speech processing, and its key idea was awarded the IEEE Spoken Language Processing Grant. Currently, she is a research scientist at Idiap Research Institute. Her research interests lie in the areas of signal processing, machine learning, statistics, acoustics, auditory scene analysis and cognition, and sparse signal recovery and acquisition.View more
Ecole Polytechnique Federale de Lausanne, Lausanne, VD, CH
Luca Baldassarre (luca.baldassarre@epfl.ch) received the M.S. degree in physics in 2006 and the Ph.D. degree in machine learning in 2010 from the University of Genoa, Italy. He then joined the Computer Science Department of University College London, United Kingdom, to work with Prof. Massimiliano Pontil on structured sparsity models for machine learning and convex optimization. Currently he is with the Laboratory for Information and Inference Systems at the Ecole Polytechnique Federale de Lausanne, Switzerland. His research interests include model-based machine learning and compressive sensing and large-scale optimization.
Luca Baldassarre (luca.baldassarre@epfl.ch) received the M.S. degree in physics in 2006 and the Ph.D. degree in machine learning in 2010 from the University of Genoa, Italy. He then joined the Computer Science Department of University College London, United Kingdom, to work with Prof. Massimiliano Pontil on structured sparsity models for machine learning and convex optimization. Currently he is with the Laboratory for Information and Inference Systems at the Ecole Polytechnique Federale de Lausanne, Switzerland. His research interests include model-based machine learning and compressive sensing and large-scale optimization.View more
