Original title: Audio Declipping with Unfolded Douglas-Rachford Algorithm
Authors: Švento, Michal
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: 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.
Keywords: audio inverse problems; declipping; deep unfolding; unrolling
Host item entry: Proceedings II of the 31st Conference STUDENT EEICT 2025: Selected papers, ISBN 978-80-214-6320-2, ISSN 2788-1334

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: https://hdl.handle.net/11012/255340

Permalink: http://www.nusl.cz/ntk/nusl-687812


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