We present a simple algorithm which accelerates the sparsity-based audio inpainting. The
algorithm optimally restricts the signal support around the missing data region. This way …

The objective of audio inpainting is to fill a gap in a signal, either to be meaningful or even to
reconstruct the original signal. We propose a novel approach applying sparse modeling in …

In this article, we deal with the problem of sparsity-based audio inpainting, ie filling in the
missing segments of audio. A consequence of the approaches based on mathematical …

The objective of audio inpainting is to fill a gap in an audio signal. This is ideally done by
reconstructing the original signal or, at least, by inferring a meaningful surrogate signal. We …

A novel sparsity-based algorithm for audio inpainting is proposed. It is an adaptation of the
SPADE algorithm by Kitić et al., originally developed for audio declipping, to the task of …

We propose the audio inpainting framework that recovers portions of audio data distorted
due to impairments such as impulsive noise, clipping, and packet loss. In this framework, the …

Audio inpainting is defined as the process of restoring the damaged segments of an audio
signal, based on the known signal values and prior information about the signal. In this …

We present a method of audio interpolation suitable for the restoration of missing and/or
corrupted audio samples. Our method assumes that the missing/corrupted samples can be …

Sasaki et al.(2018) presented an efficient audio declipping algorithm, based on the
properties of Hankel-structure matrices constructed from time-domain signal blocks. We …

Reweighted l1 minimization has been successfully applied to the task of audio declipping by
Weinstein and Wakin (2011). We adapt the reweighting to the audio inpainting problem, for …