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Found 7 papers, 5 papers with code
On the Use of Autoregressive Methods for Audio Inpainting
The paper presents an evaluation of popular audio inpainting methods based on autoregressive modelling, namely, extrapolation-based and Janssen methods.
Improving DCE-MRI through unfolded low-rank + sparse optimisation
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
Sasaki et al. (2018) presented an efficient audio declipping algorithm, based on the properties of Hankel-structure matrices constructed from time-domain signal blocks.
Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization
First, we treat the missing samples as latent variables, and derive two expectation-maximization algorithms for estimating the parameters of the model, depending on whether we formulate the problem in the time- or time-frequency domain.
Audio declipping performance enhancement via crossfading
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.
Audio Dequantization Using (Co)Sparse (Non)Convex Methods
The paper deals with the hitherto neglected topic of audio dequantization.
Approximal operator with application to audio inpainting
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.
