Audio Declipping with Unfolded Douglas-Rachford Algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0197732" target="_blank" >RIV/00216305:26220/26:0197732 - isvavai.cz</a>

Výsledek na webu

<a href="https://dspace.vut.cz/items/3ddfdf2f-e304-436a-9e23-1bbe58d83fae" target="_blank" >https://dspace.vut.cz/items/3ddfdf2f-e304-436a-9e23-1bbe58d83fae</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.13164/eeict.2025.145" target="_blank" >10.13164/eeict.2025.145</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Audio Declipping with Unfolded Douglas-Rachford Algorithm

Popis výsledku v původním jazyce

This paper addresses the problem of audio declipping, which occurs when audio signals exceed a certain level, causing distortion and loss of information. To enhance existing methods, we propose a novel solution combining deep unfolding with the Douglas–Rachford algorithm (DRA) within an optimization framework, offering a blend of deep learning and optimization. The declipping problem is formulated as an optimization task that aims to recover the original signal by minimizing sparsity in the time-frequency domain. Our approach transforms each iteration of DRA into a layer of a neural network, optimizing parameters based on training data. Experimental results demonstrate that the unrolled DRA (uDRA) achieves short inference time compared to classical declipping methods, although it does not yet match them in terms of restoration quality. This work highlights the potential of deep unfolding for efficient audio declipping, with future improvements needed to capture the complexities of audio distortion more effectively.

Název v anglickém jazyce

Audio Declipping with Unfolded Douglas-Rachford Algorithm

Popis výsledku anglicky

This paper addresses the problem of audio declipping, which occurs when audio signals exceed a certain level, causing distortion and loss of information. To enhance existing methods, we propose a novel solution combining deep unfolding with the Douglas–Rachford algorithm (DRA) within an optimization framework, offering a blend of deep learning and optimization. The declipping problem is formulated as an optimization task that aims to recover the original signal by minimizing sparsity in the time-frequency domain. Our approach transforms each iteration of DRA into a layer of a neural network, optimizing parameters based on training data. Experimental results demonstrate that the unrolled DRA (uDRA) achieves short inference time compared to classical declipping methods, although it does not yet match them in terms of restoration quality. This work highlights the potential of deep unfolding for efficient audio declipping, with future improvements needed to capture the complexities of audio distortion more effectively.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/GA23-07294S" target="_blank" >GA23-07294S: Od perceptronu k percepci: psychoakusticky motivovaná rekonstrukce audio signálu s využitím prvků hlubokého učení</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings II of the 31st Student EEICT 2025 (Selected Papers)

ISBN

978-80-214-6320-2

ISSN

—

e-ISSN

2788-1334

Počet stran výsledku

4

Strana od-do

145-148

Název nakladatele

—

Místo vydání

—

Místo konání akce

Brno

Datum konání akce

29. 4. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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