28 citations · 90 across the 12 of their papers we have counts for
12 papers
Regularized autoregressive modeling and its application to audio signal reconstruction
Ondřej Mokrý, Pavel Rajmic
Autoregressive (AR) modeling is invaluable in signal processing, in particular in speech and audio fields. Attempts in the literature can be found that regularize or constrain eith…
Janssen 2.0: Audio Inpainting in the Time-frequency Domain
Ondřej Mokrý, Peter Balušík, Pavel Rajmic
The paper focuses on inpainting missing parts of an audio signal spectrogram, i.e., estimating the lacking time-frequency coefficients. The autoregression-based Janssen algorithm,…
Tweaking autoregressive methods for inpainting of gaps in audio signals
Ondřej Mokrý, Pavel Rajmic
A novel variant of the Janssen method for audio inpainting is presented and compared to other popular audio inpainting methods based on autoregressive (AR) modeling. Both conceptua…
Improving DCE-MRI through unfolded low-rank + sparse optimisation
Ondřej Mokrý, Jiří Vitouš, Pavel Rajmic +1
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.…
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 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…
Approximal operator with application to audio inpainting
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 proxi…
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…
Audio Inpainting: Revisited and Reweighted
Ondřej Mokrý, Pavel Rajmic
We deal with the problem of sparsity-based audio inpainting, i.e. filling in the missing segments of audio. A consequence of the approaches based on mathematical optimization is th…
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…