Detail publikace

Audio Inpainting: Revisited and Reweighted

MOKRÝ, O. RAJMIC, P.

Originální název

Audio Inpainting: Revisited and Reweighted

Anglický název

Audio Inpainting: Revisited and Reweighted

Jazyk

en

Originální abstrakt

In this article, we deal with the problem of sparsity-based audio inpainting, i.e. filling in the missing segments of audio. A consequence of the approaches based on mathematical optimization is the insufficient amplitude of the signal in the filled gaps. Remaining in the framework based on sparsity and convex optimization, we propose improvements to audio inpainting, aiming at compensating for such an energy loss. The new ideas are based on different types of weighting, both in the coefficient and the time domains. We show that our propositions improve the inpainting performance in terms of both the SNR and ODG.

Anglický abstrakt

In this article, we deal with the problem of sparsity-based audio inpainting, i.e. filling in the missing segments of audio. A consequence of the approaches based on mathematical optimization is the insufficient amplitude of the signal in the filled gaps. Remaining in the framework based on sparsity and convex optimization, we propose improvements to audio inpainting, aiming at compensating for such an energy loss. The new ideas are based on different types of weighting, both in the coefficient and the time domains. We show that our propositions improve the inpainting performance in terms of both the SNR and ODG.

Dokumenty

BibTex


@article{BUT164794,
  author="Ondřej {Mokrý} and Pavel {Rajmic}",
  title="Audio Inpainting: Revisited and Reweighted",
  annote="In this article, we deal with the problem of sparsity-based audio inpainting, i.e. filling in the missing segments of audio. A consequence of the approaches based on mathematical optimization is the insufficient amplitude of the signal in the filled gaps. Remaining in the framework based on sparsity and convex optimization, we propose improvements to audio inpainting, aiming at compensating for such an energy loss. The new ideas are based on different types of weighting, both in the coefficient and the time domains. We show that our propositions improve the inpainting performance in terms of both the SNR and ODG.",
  address="IEEE",
  chapter="164794",
  doi="10.1109/TASLP.2020.3030486",
  howpublished="online",
  institution="IEEE",
  number="28",
  year="2020",
  month="october",
  pages="2906--2918",
  publisher="IEEE",
  type="journal article in Web of Science"
}