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Sparse non-local similarity modeling for audio inpainting

Ichrak Toumi 1 Valentin Emiya 1 
1 QARMA - éQuipe d'AppRentissage de MArseille
LIS - Laboratoire d'Informatique et Systèmes
Abstract : Audio signals are highly structured from a low, signal level to high cognitive aspects. We investigate how to exploit the common sparse structure between similar audio frames in order to reconstruct missing data in audio signals. While joint sparse models and related algorithms have been widely studied, one important challenge is to locate such similar frames : the search must be adapted to the joint-sparse model and should be fast and one must deal with missing data in the frames. We propose, compare and discuss several similarity measures dedicated to this task. We then show how this strategy can lead to better reconstruction of missing data in audio signals.
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Submitted on : Thursday, January 11, 2018 - 8:21:05 AM
Last modification on : Thursday, July 14, 2022 - 4:05:52 AM
Long-term archiving on: : Wednesday, May 23, 2018 - 6:21:05 PM


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  • HAL Id : hal-01680669, version 1



Ichrak Toumi, Valentin Emiya. Sparse non-local similarity modeling for audio inpainting. ICASSP - IEEE International Conference on Acoustics, Speech and Signal Processing, Apr 2018, Calgary, Canada. ⟨hal-01680669⟩



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