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Abstract
Audio source separation is an extremely useful process but most of the time not a goal by itself. Even though most research focuses on better separation quality, ultimately separation is needed so that we can perform tasks such as noisy speech recognition, music analysis, single-source editing, etc. In this talk I’ll present some recent work on audio source separation that extends the idea of basis functions to that of using ‘exemplars’ and then builds off that idea in order to provide direct computation of some of the above goals without having to resort to an intermediate separation step. In order to do so I’ll discuss some of the interesting geometric properties of mixed audio signals and how one can employ massively large decommissions with aggressive sparsity settings in order to achieve the above results.
Biography
Paris Smaragdis is faculty in the Computer Science and the Electrical and Computer Science departments at the University of Illinois at Urbana-Champaign. He completed his graduate and postdoctoral studies at MIT, where he conducted research on computational perception and audio processing. Prior to the University of Illinois he was a senior research scientist at Adobe Systems and a research scientist at Mitsubishi Electric Research Labs, during which time he was selected by the MIT Technology Review as one of the top 35 young innovators of 2006. Paris’ research interests lie in the intersection of machine learning and signal processing, especially as they apply to audio problems.