1 code implementation • 7 Mar 2024 • Ondřej Mokrý, Pavel Rajmic
The paper presents an evaluation of popular audio inpainting methods based on autoregressive modelling, namely, extrapolation-based and Janssen methods.
no code implementations • 12 Dec 2023 • Ondřej Mokrý, Jiří Vitouš, Pavel Rajmic, Radovan Jiřík
A method for perfusion imaging with DCE-MRI is developed based on two popular paradigms: the low-rank + sparse model for optimisation-based reconstruction, and the deep unfolding.
no code implementations • 31 Mar 2023 • 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.
1 code implementation • 20 May 2022 • Pavel Záviška, Pavel Rajmic
We develop the analysis (cosparse) variant of the popular audio declipping algorithm of Siedenburg et al. (2014).
1 code implementation • 7 Apr 2021 • 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.
1 code implementation • 30 Oct 2020 • Pavel Záviška, Pavel Rajmic, Ondřej Mokrý
The paper deals with the hitherto neglected topic of audio dequantization.
1 code implementation • 15 Jul 2020 • Pavel Záviška, Pavel Rajmic, Alexey Ozerov, Lucas Rencker
Audio declipping algorithms often make assumptions about the underlying signal, such as sparsity or low-rankness, and about the measurement system.
1 code implementation • 4 May 2020 • Ondřej Mokrý, Pavel Rajmic
In their recent evaluation of time-frequency representations and structured sparsity approaches to audio inpainting, Lieb and Stark (2018) have used a particular mapping as a proximal operator.
no code implementations • 5 Mar 2020 • Pavel Záviška, Pavel Rajmic
The paper shows the potential of sparsity-based methods in restoring quantized signals.