Audio Inpainting: Revisited and Reweighted
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We deal with the problem of sparsity-based audio inpainting. A consequence of optimization approaches is actually the insufficient energy of the signal within the filled gap. We propose improvements to the audio inpainting framework based on sparsity and convex optimization, aiming at compensating for this energy loss. The new ideas are based on different types of weighting, both in the coefficient and the time domains. We show that our propositions improve the inpainting performance both in terms of the SNR and ODG. However, the autoregressive Janssen algorithm remains a strong competitor.
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