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 …

We present a novel sparse representation based approach for the restoration of clipped
audio signals. In the proposed approach, the clipped signal is decomposed into overlapping …

In this article, we study the ability of deep neural networks (DNNs) to restore missing audio
content based on its context, ie, inpaint audio gaps. We focus on a condition which has not …

This paper proposes a novel patch-wise image inpainting algorithm using the image signal
sparse representation over a redundant dictionary, which merits in both capabilities to deal …

Dynamic range limitations in signal processing often lead to clipping, or saturation, in
signals. The task of audio declipping is estimating the original audio signal, given its clipped …

We present a method to address the (inherently ill-posed) problem of missing data
interpolation over repeated short gaps in audio signals. By formulating the problem in terms …

In this paper, we propose a new video inpainting method which applies to both static or free-
moving camera videos. The method can be used for object removal, error concealment, and …

We propose SpeechPainter, a model for filling in gaps of up to one second in speech
samples by leveraging an auxiliary textual input. We demonstrate that the model performs …

This chapter is concerned with the application of modern signal processing techniques to
the restoration of degraded audio signals. Although attention is focussed on gramophone …

With the development of audio playback devices and fast data transmission, the demand for
high sound quality is rising for both entertainment and communications. In this quest for …