Regularized autoregressive modeling and its application to audio signal reconstruction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0200901" target="_blank" >RIV/00216305:26220/26:0200901 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/11371707" target="_blank" >https://ieeexplore.ieee.org/document/11371707</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TASLPRO.2026.3661299" target="_blank" >10.1109/TASLPRO.2026.3661299</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Regularized autoregressive modeling and its application to audio signal reconstruction

Popis výsledku v původním jazyce

Autoregressive (AR) modeling is invaluable in signal processing, in particular in speech and audio fields. Attempts in the literature can be found that regularize or constrain either the time-domain signal values or the AR coefficients, which is done for various reasons, including the incorporation of prior information or numerical stabilization. Although these attempts are appealing, an encompassing and generic modeling framework is still missing. We propose such a framework and the related optimization problem and algorithm. We discuss the computational demands of the algorithm and explore the effects of various improvements on its convergence speed. In the experimental part, we demonstrate the usefulness of our approach on the audio declipping and dequantization problems. We compare its performance against state-of-the-art methods and demonstrate the competitiveness of the proposed method in declipping musical signals, and its superiority in declipping speech. The evaluation includes a heuristic algorithm of generalized linear prediction (GLP), a strong competitor which has only been presented as a patent and is new in the scientific community.

Název v anglickém jazyce

Regularized autoregressive modeling and its application to audio signal reconstruction

Popis výsledku anglicky

Autoregressive (AR) modeling is invaluable in signal processing, in particular in speech and audio fields. Attempts in the literature can be found that regularize or constrain either the time-domain signal values or the AR coefficients, which is done for various reasons, including the incorporation of prior information or numerical stabilization. Although these attempts are appealing, an encompassing and generic modeling framework is still missing. We propose such a framework and the related optimization problem and algorithm. We discuss the computational demands of the algorithm and explore the effects of various improvements on its convergence speed. In the experimental part, we demonstrate the usefulness of our approach on the audio declipping and dequantization problems. We compare its performance against state-of-the-art methods and demonstrate the competitiveness of the proposed method in declipping musical signals, and its superiority in declipping speech. The evaluation includes a heuristic algorithm of generalized linear prediction (GLP), a strong competitor which has only been presented as a patent and is new in the scientific community.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

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

2026

Kód důvěrnosti údajů

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

Název periodika

IEEE transactions on audio, speech, and language processing

ISSN

—

e-ISSN

2998-4173

Svazek periodika

—

Číslo periodika v rámci svazku

34

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

1312-1325

Kód UT WoS článku

001700561000002

EID výsledku v databázi Scopus

2-s2.0-105029968681