Publication detail

Audio Declipping with (Weighted) Analysis Social Sparsity

ZÁVIŠKA, P. RAJMIC, P.

Original Title

Audio Declipping with (Weighted) Analysis Social Sparsity

Type

conference paper

Language

English

Original Abstract

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.

Keywords

audio declipping;cosparse;sparse;social sparsity;weighting

Authors

ZÁVIŠKA, P.; RAJMIC, P.

Released

18. 8. 2022

Publisher

IEEE

Location

Prague, Czech republic

ISBN

978-1-6654-6948-7

Book

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

Pages from

407

Pages to

412

Pages count

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"
}