Regularized autoregressive modeling and its application to audio signal reconstruction
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Regularized autoregressive modeling and its application to audio signal reconstruction
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
<a href="/en/project/GA23-07294S" target="_blank" >GA23-07294S: From perceptron to perception: psychoacoustically motivated audio reconstruction using learned components</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2026
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE transactions on audio, speech, and language processing
ISSN
—
e-ISSN
2998-4173
Volume of the periodical
—
Issue of the periodical within the volume
34
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
1312-1325
UT code for WoS article
001700561000002
EID of the result in the Scopus database
2-s2.0-105029968681