Detail publikace

Audio Declipping with (Weighted) Analysis Social Sparsity

ZÁVIŠKA, P. RAJMIC, P.

Originální název

Audio Declipping with (Weighted) Analysis Social Sparsity

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

We develop the analysis (cosparse) variant of the popular audio declipping algorithm of Siedenburg et al. (2014). Furthermore, we extend both the old and the new variants by the possibility of weighting the time-frequency coefficients. We examine the audio reconstruction performance of several combinations of weights and shrinkage operators. The weights are shown to improve the reconstruction quality in some cases; however, the best scores achieved by the non-weighted methods are not surpassed with the help of weights. Yet, the analysis Empirical Wiener (EW) shrinkage was able to reach the quality of a computationally more expensive competitor, the Persistent Empirical Wiener (PEW). Moreover, the proposed analysis variant incorporating PEW slightly outperforms the synthesis counterpart in terms of an auditorily motivated metric.

Klíčová slova

audio declipping;cosparse;sparse;social sparsity;weighting

Autoři

ZÁVIŠKA, P.; RAJMIC, P.

Vydáno

18. 8. 2022

Nakladatel

IEEE

Místo

Prague, Czech republic

ISBN

978-1-6654-6948-7

Kniha

Proceedings of the 2022 45th International Conference on Telecommunications and Signal Processing (TSP)

Strany od

407

Strany do

412

Strany počet

6

URL

https://ieeexplore.ieee.org/document/9851269

BibTex

@inproceedings{BUT178520,
  author="Pavel {Záviška} and Pavel {Rajmic}",
  title="Audio Declipping with (Weighted) Analysis Social Sparsity",
  booktitle="
Proceedings of the 2022 45th International Conference on Telecommunications and Signal Processing (TSP)",
  year="2022",
  pages="407--412",
  publisher="IEEE",
  address="Prague, Czech republic",
  doi="10.1109/TSP55681.2022.9851269",
  isbn="978-1-6654-6948-7",
  url="https://ieeexplore.ieee.org/document/9851269"
}