7 papers
A survey and an extensive evaluation of popular audio declipping methods
Pavel Záviška, Pavel Rajmic, Alexey Ozerov +1
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 cl…
Flexible framework for audio reconstruction
Ondřej Mokrý, Pavel Rajmic, Pavel Záviška
The paper presents a unified, flexible framework for the tasks of audio inpainting, declipping, and dequantization. The concept is further extended to cover analogous degradation m…
Sparse and Cosparse Audio Dequantization Using Convex Optimization
Pavel Záviška, Pavel Rajmic
The paper shows the potential of sparsity-based methods in restoring quantized signals. Following up on the study of Brauer et al. (IEEE ICASSP 2016), we significantly extend the r…
Psychoacoustically Motivated Audio Declipping Based on Weighted l1 Minimization
Pavel Záviška, Pavel Rajmic, Jíří Schimmel
A novel method for audio declipping based on sparsity is presented. The method incorporates psychoacoustic information by weighting the transform coefficients in the minim…
Introducing SPAIN (SParse Audio INpainter)
Ondřej Mokrý, Pavel Záviška, Pavel Rajmic +1
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 tas…
A Proper version of Synthesis-based Sparse Audio Declipper
Pavel Záviška, Pavel Rajmic, Ondřej Mokrý +1
Methods based on sparse representation have found great use in the recovery of audio signals degraded by clipping. The state of the art in declipping has been achieved by the SPADE…