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Audio Signal Declipping and Dequantization Using Sparsity-Based Methods
Záviška, Pavel ; Šroubek,, Filip (oponent) ; Koldovský,, Zbyněk (oponent) ; Rajmic, Pavel (vedoucí práce)
Audio signals are susceptible to various types of quality degradation, with clipping being one of the most common and problematic distortions. This Thesis addresses the restoration of audio signals corrupted by nonlinear distortions and presents the contribution in the field of sparsity-based audio restoration algorithms, with the main focus on audio declipping and dequantization. The first part of the Thesis deals with the problem of audio declipping and presents several sparsity-based approaches, containing both the original research and adopted algorithms, which have been reimplemented or modified. The performance of the algorithms is evaluated using the Signal-to-Distortion ratio, as well as perceptually motivated metrics of sound quality. Then, attention is paid on incorporating psychoacoustic information into declipping by weighting the transform coefficients. Three possible constructions of the weights are presented and it is shown that with correctly chosen weights, it is possible to significantly improve the performance of the algorithms, which achieve state-of-the-art restoration quality with low computational complexity. Special focus is also paid on declipping methods that allow a deviation in the reliable part. In that direction, the Thesis studies the perceptual effects of plain replacement of the reliable samples, then identifies its main weaknesses and introduces methods to compensate the discovered negative effects. It is shown that using this technique, it is possible to enhance the performance of such declipping algorithms without a significant increase in computational complexity. Finally, selected declipping algorithms are adopted to the problem of audio dequantization. The Thesis is accompanied by repositories containing implementations of the presented methods.
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