Přístupnostní navigace
E-přihláška
Vyhledávání Vyhledat Zavřít
Detail publikace
ZÁVIŠKA, P. RAJMIC, P. PRŮŠA, Z. VESELÝ, V.
Originální název
Revisiting synthesis model in Sparse Audio Declipper
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
The state of the art in audio declipping has currently been achieved by SPADE (SParse Audio DEclipper) algorithm by Kitic et al. Until now, the synthesis/sparse variant, S-SPADE, has been considered significantly slower than its analysis/cosparse counterpart, A-SPADE. It turns out that the opposite is true: by exploiting a recent projection lemma, individual iterations of both algorithms can be made equally computationally expensive, while S-SPADE tends to require considerably fewer iterations to converge. In this paper, the two algorithms are compared across a range of parameters such as the window length, window overlap and redundancy of the transform. The experiments show that although S-SPADE typically converges faster, the average performance in terms of restoration quality is not superior to A-SPADE.
Klíčová slova
Clipping, Declipping, Audio, Sparse, Cosparse, SPADE, Projection, Restoration
Autoři
ZÁVIŠKA, P.; RAJMIC, P.; PRŮŠA, Z.; VESELÝ, V.
Vydáno
2. 7. 2018
Nakladatel
Springer
Místo
Cham
ISBN
978-3-319-93764-9
Kniha
Latent Variable Analysis and Signal Separation, 14th International Conference, LVA/ICA 2018 Proceedings
Edice
Lecture Notes in Computer Science
Číslo edice
10891
Strany od
429
Strany do
445
Strany počet
17
URL
https://link.springer.com/book/10.1007%2F978-3-319-93764-9
BibTex
@inproceedings{BUT146951, author="Pavel {Záviška} and Pavel {Rajmic} and Zdeněk {Průša} and Vítězslav {Veselý}", title="Revisiting synthesis model in Sparse Audio Declipper", booktitle="Latent Variable Analysis and Signal Separation, 14th International Conference, LVA/ICA 2018 Proceedings", year="2018", series="Lecture Notes in Computer Science", number="10891", pages="429--445", publisher="Springer", address="Cham", doi="10.1007/978-3-319-93764-9\{_}40", isbn="978-3-319-93764-9", url="https://link.springer.com/book/10.1007%2F978-3-319-93764-9" }