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ZÁVIŠKA, P.; RAJMIC, P.
Original Title
Sparse and Cosparse Audio Dequantization Using Convex Optimization
English Title
Type
Paper in proceedings (conference paper)
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
Keywords
Quantizaiton; Dequantization; Sparsity; Cosparsity; Proximal splitting
Key words in English
Authors
RIV year
2021
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
https://ieeexplore.ieee.org/document/9163566
BibTex
@inproceedings{BUT164024, author="Pavel {Záviška} and Pavel {Rajmic}", title="Sparse and Cosparse Audio Dequantization Using Convex Optimization", booktitle=" Proceedings of the 2020 43rd International Conference on Telecommunications and Signal Processing (TSP)", year="2020", pages="216--220", publisher="IEEE", address="Milan, Italy", doi="10.1109/TSP49548.2020.9163566", isbn="978-1-7281-6376-5", url="https://ieeexplore.ieee.org/document/9163566" }