Sparse non-local similarity modeling for audio inpainting

Ichrak Toumi (1) , Valentin Emiya (1)

1 QARMA - éQuipe d'AppRentissage de MArseille

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.

Mots clés: Sparse approximation; matching pursuit; audio; joint sparsity; inpainting

Domaines: Traitement du signal et de l'image [eess.SP]; Apprentissage [cs.LG]; Son [cs.SD]

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⟩