Estimation and Restoration of Unknown Nonlinear Distortion using Diffusion
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0197853" target="_blank" >RIV/00216305:26220/26:0197853 - isvavai.cz</a>
Result on the web
<a href="https://aes2.org/publications/elibrary-page/?id=22953" target="_blank" >https://aes2.org/publications/elibrary-page/?id=22953</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.17743/jaes.2022.0221" target="_blank" >10.17743/jaes.2022.0221</a>
Alternative languages
Result language
angličtina
Original language name
Estimation and Restoration of Unknown Nonlinear Distortion using Diffusion
Original language description
The restoration of nonlinearly distorted audio signals, alongside the identification of the applied memoryless nonlinear operation, is studied. The paper focuses on the difficult but practically important case in which both the nonlinearity and the original input signal are unknown. The proposed method uses a generative diffusion model trained unconditionally on guitar or speech signals to jointly model and invert the nonlinearsystem at inference time. Both the memoryless nonlinear function model and the restored audio signal are obtained as output. Examples of successful blind estimation of hard and soft clipping, digital quantization, half-wave rectification, and wavefolding nonlinearities are presented. Our results suggest that, out of the nonlinear functions tested here, the cubic Catmull-Rom spline is best suited to approximating these nonlinearities. In the case of guitar recordings, comparisons with informed and supervised restoration methods show that the proposed blind method is at least as good as they are in terms of objective metrics. Experiments on distorted speech show that the proposed blind method outperforms general-purpose speech enhancement techniques and restores the original voice quality. The proposed method can be applied to memoryless audio effects modeling, restoration of music and speech recordings, and characterization of analog recording media.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20203 - Telecommunications
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
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of the Audio Engineering Society
ISSN
1549-4950
e-ISSN
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Volume of the periodical
73
Issue of the periodical within the volume
9
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
519-532
UT code for WoS article
001571323900003
EID of the result in the Scopus database
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