Date/heure
Date(s) - 28 mars 2013

Catégories Pas de Catégories

$Social\; Sparsity:\; application\; to\; audio\; inpainting.\; By\; Matthieu\; Kowalski,\; L2S.\; Abstract:\; Audio\; inpainting\; problem\; is\; under\; consideration,\; using\; iterative\; thresholding\; algorithms\; build\; on\; the\; “social\; sparsity”\; principle.\; First,\; we\; present\; new\; shrinkage\; operators,\; allowing\; one\; to\; take\; into\; account\; the\; neighborhood\; of\; time-frequency\; coefficients.\; Then,\; the\; audio\; declipping\; problem\; is\; formulated\; as\; a\; unconstrained\; convex\; optimization\; problem,\; but\; taking\; into\; account\; an\; inportant\; hypothesis\; of\; audio\; declipping:\; reconstructed\; samples\; must\; be\; greater\; than\; the\; clipping\; threshold.\; The\; structured\; thresholding\; operators,\; such\; as\; the\; windowed\; group-Lasso\; or\; the\; persistent\; empirical\; wiener,\; are\; embedded\; into\; iterative\; algorithms,\; and\; we\; show\; on\; experimental\; results\; the\; SNR\; improvement\; compared\; to\; a\; more\; conventional\; Lasso.\; We\; also\; compare\; the\; results\; to\; the\; state\; of\; the\; art\; audio\; declipping.\; Download\; slides$

Date/heure
Date(s) - 28 mars 2013

Catégories Pas de Catégories

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