M. Kowalski (L2S): Social Sparsity: application to audio inpainting.

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Date(s) - 28/03/2013
14 h 00 min - 15 h 00 min

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Social Sparsity: application to audio inpainting.\n\nBy Matthieu Kowalski\, L2S.\n\nAbstract:\nAudio inpainting problem is under consideration\, using iterative\nthresholding algorithms build on the “social sparsity” principle.\nFirst\, we present new shrinkage operators\, allowing one to take into\naccount the neighborhood of time-frequency coefficients. Then\, the\naudio declipping problem is formulated as a unconstrained convex\noptimization problem\, but taking into account an inportant hypothesis\nof audio declipping: reconstructed samples must be greater than the\nclipping threshold. The structured thresholding operators\, such as the\nwindowed group-Lasso or the persistent empirical wiener\, are embedded\ninto iterative algorithms\, and we show on experimental results the SNR\nimprovement compared to a more conventional Lasso. We also compare the\nresults to the state of the art audio declipping.\n\nDownload slides

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