Audio Dequantization Using (Co)Sparse (Non)Convex Methods
TL
TL;DR
The paper reviews the state-of-the-art sparsity-based approaches and proposes several new methods for audio dequantization, and all the presented formulations come in both the synthesis and analysis variants.
Abstract
The paper deals with the hitherto neglected topic of audio dequantization. It reviews the state-of-the-art sparsity-based approaches and proposes several new methods. Convex as well as non-convex approaches are included, and all the presented formulations come in both the synthesis and analysis variants. In the experiments the methods are evaluated using the signal-to-distortion ratio (SDR) and PEMO-Q, a perceptually motivated metric.
References35 items
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A Survey and an Extensive Evaluation of Popular Audio Declipping Methods
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Approximal operator with application to audio inpainting
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Flexible framework for audio reconstruction
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Sparse and Cosparse Audio Dequantization Using Convex Optimization
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