18 citations · 20 across the 5 of their papers we have counts for
6 papers
Audio Decoding by Inverse Problem Solving
Pedro J. Villasana T., Lars Villemoes, Janusz Klejsa +1
We consider audio decoding as an inverse problem and solve it through diffusion posterior sampling. Explicit conditioning functions are developed for input signal measurements prov…
Distribution Preserving Source Separation With Time Frequency Predictive Models
Pedro J. Villasana T., Janusz Klejsa, Lars Villemoes +1
We provide an example of a distribution preserving source separation method, which aims at addressing perceptual shortcomings of state-of-the-art methods. Our approach uses uncondi…
Source coding of audio signals with a generative model
Roy Fejgin, Janusz Klejsa, Lars Villemoes +1
We consider source coding of audio signals with the help of a generative model. We use a construction where a waveform is first quantized, yielding a finite bitrate representation.…
High-quality speech coding with SampleRNN
Janusz Klejsa, Per Hedelin, Cong Zhou +2
We provide a speech coding scheme employing a generative model based on SampleRNN that, while operating at significantly lower bitrates, matches or surpasses the perceptual quality…
Optimal Index Assignment for Multiple Description Scalar Quantization
Guoqiang Zhang, Janusz Klejsa, W. Bastiaan Kleijn
We provide a method for designing an optimal index assignment for scalar K-description coding. The method stems from a construction of translated scalar lattices, which provides a…
On Distribution Preserving Quantization
Minyue Li, Janusz Klejsa, W. Bastiaan Kleijn
Upon compressing perceptually relevant signals, conventional quantization generally results in unnatural outcomes at low rates. We propose distribution preserving quantization (DPQ…