Publication detail

Sparse and Cosparse Audio Dequantization Using Convex Optimization

ZÁVIŠKA, P. RAJMIC, P.

Original Title

Sparse and Cosparse Audio Dequantization Using Convex Optimization

English Title

Sparse and Cosparse Audio Dequantization Using Convex Optimization

Type

conference paper

Language

en

Original Abstract

The paper shows the potential of sparsity-based methods in restoring quantized signals. Following up on the study of Brauer et al. (IEEE ICASSP 2016), we significantly extend the range of the evaluation scenarios: we introduce the analysis (cosparse) model, we use more effective algorithms, we experiment with another time-frequency transform. The paper shows that the analysis-based model performs comparably to the synthesis-model, but the Gabor transform produces better results than the originally used cosine transform. Last but not least, we provide codes and data in a reproducible way.

English abstract

The paper shows the potential of sparsity-based methods in restoring quantized signals. Following up on the study of Brauer et al. (IEEE ICASSP 2016), we significantly extend the range of the evaluation scenarios: we introduce the analysis (cosparse) model, we use more effective algorithms, we experiment with another time-frequency transform. The paper shows that the analysis-based model performs comparably to the synthesis-model, but the Gabor transform produces better results than the originally used cosine transform. Last but not least, we provide codes and data in a reproducible way.

Keywords

Quantizaiton; Dequantization; Sparsity; Cosparsity; Proximal splitting

Released

11.08.2020

Publisher

IEEE

Location

Milan, Italy

ISBN

978-1-7281-6376-5

Book

Proceedings of the 2020 43rd International Conference on Telecommunications and Signal Processing (TSP)

Pages from

216

Pages to

220

Pages count

5

URL

Documents

BibTex


@inproceedings{BUT164024,
  author="Pavel {Záviška} and Pavel {Rajmic}",
  title="Sparse and Cosparse Audio Dequantization Using Convex Optimization",
  annote="The paper shows the potential of sparsity-based methods in restoring quantized signals. Following up on the study of Brauer et al. (IEEE ICASSP 2016), we significantly extend the range of the evaluation scenarios: we introduce the analysis (cosparse) model, we use more effective algorithms, we experiment with another time-frequency transform. The paper shows that the analysis-based model performs comparably to the synthesis-model, but the Gabor transform produces better results than the originally used cosine transform. Last but not least, we provide codes and data in a reproducible way.",
  address="IEEE",
  booktitle="
Proceedings of the 2020 43rd International Conference on Telecommunications and Signal Processing (TSP)",
  chapter="164024",
  doi="10.1109/TSP49548.2020.9163566",
  howpublished="online",
  institution="IEEE",
  year="2020",
  month="august",
  pages="216--220",
  publisher="IEEE",
  type="conference paper"
}