Revisiting synthesis model in Sparse Audio Declipper

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU127599" target="_blank" >RIV/00216305:26220/18:PU127599 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/book/10.1007%2F978-3-319-93764-9" target="_blank" >https://link.springer.com/book/10.1007%2F978-3-319-93764-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-93764-9_40" target="_blank" >10.1007/978-3-319-93764-9_40</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Revisiting synthesis model in Sparse Audio Declipper

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Revisiting synthesis model in Sparse Audio Declipper

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/GF17-33798L" target="_blank" >GF17-33798L: Moderní metody restaurace chybějících dat v digitálních audiosignálech</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Latent Variable Analysis and Signal Separation, 14th International Conference, LVA/ICA 2018 Proceedings

ISBN

978-3-319-93764-9

ISSN

—

e-ISSN

—

Počet stran výsledku

17

Strana od-do

429-445

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Guildford

Datum konání akce

2. 7. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000521730400040