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.
Complete list of metadatas

Cited literature [14 references]  Display  Hide  Download

https://hal-amu.archives-ouvertes.fr/hal-01680669
Contributor : Valentin Emiya <>
Submitted on : Thursday, January 11, 2018 - 8:21:05 AM
Last modification on : Monday, April 30, 2018 - 9:58:01 AM
Long-term archiving on : Wednesday, May 23, 2018 - 6:21:05 PM

File

PaperICASSP_submitted.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01680669, version 1

Collections

Citation

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⟩

Share

Metrics

Record views

589

Files downloads

321