AUDASCITY: AUdio Denoising by Adaptive Social CosparsITY

Abstract : This work aims at introducing a new algorithm, AUDASCITY, and comparing its performance to the time-frequency block thresholding algorithm for the ill-posed problem of audio denoising. We propose a heuristics which combines time-frequency structure, cosparsity, and an adaptive scheme to denoise audio signals corrupted with white noise. We report that AUDASCITY outperforms state-of-the-art for each numerical comparison. While there is still room for some perceptual improvements, AUDASCITY's usefulness is shown when used as a front-end for a classification task.
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Communication dans un congrès
25th European Signal Processing Conference (EUSIPCO), Aug 2017, Kos, Greece. 2017
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https://hal.inria.fr/hal-01540945
Contributeur : Clément Gaultier <>
Soumis le : vendredi 16 juin 2017 - 17:39:44
Dernière modification le : mercredi 29 novembre 2017 - 15:43:20

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AudascityEUSIPCO_CR.pdf
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  • HAL Id : hal-01540945, version 1

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Clément Gaultier, Srđan Kitić, Nancy Bertin, Rémi Gribonval. AUDASCITY: AUdio Denoising by Adaptive Social CosparsITY. 25th European Signal Processing Conference (EUSIPCO), Aug 2017, Kos, Greece. 2017. 〈hal-01540945〉

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