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 …

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 …

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 …

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 present a new framework for the restoration of missing samples in audio signals. It
consists in locating audio frames that share similar sparse structures and in applying a joint …

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 paper focuses on inpainting missing parts of an audio signal spectrogram. First, a recent
successful approach based on an untrained neural network is revised and its several …

This research presents a novel hybrid audio inpainting approach that considers the diversity
of signals and enhances the reconstruction quality. Existing inpainting approaches have …

Audio inpainting restores locally corrupted parts of digital audio signals. Sparsity-based
methods achieve this by promoting sparsity in the time-frequency (TF) domain, assuming …

Audio inpainting aims to reconstruct missing segments in corrupted recordings. Most of
existing methods produce plausible reconstructions when the gap lengths are short, but …