51 citations · 132 across the 11 of their papers we have counts for
11 papers
Multiple Hankel matrix rank minimization for audio inpainting
Pavel Záviška, Pavel Rajmic, Ondřej Mokrý
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 adapt th…
Audio Declipping with (Weighted) Analysis Social Sparsity
Pavel Záviška, Pavel Rajmic
We develop the analysis (cosparse) variant of the popular audio declipping algorithm of Siedenburg et al. (2014). Furthermore, we extend both the old and the new variants by the po…
Audio declipping performance enhancement via crossfading
Pavel Záviška, Pavel Rajmic, Ondřej Mokrý
Some audio declipping methods produce waveforms that do not fully respect the physical process of clipping, which is why we refer to them as inconsistent. This letter reports what…
Audio Dequantization Using (Co)Sparse (Non)Convex Methods
Pavel Záviška, Pavel Rajmic, Ondřej Mokrý
The paper deals with the hitherto neglected topic of audio dequantization. It reviews the state-of-the-art sparsity-based approaches and proposes several new methods. Convex as wel…
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…
Revisiting Synthesis Model of Sparse Audio Declipper
Pavel Záviška, Pavel Rajmic, Zdeněk Průša +1
The state of the art in audio declipping has currently been achieved by SPADE (SParse Audio DEclipper) algorithm by Kitić et al. Until now, the synthesis/sparse variant, S-SPADE, h…