1 citations · 1 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…
Generative Machine Listener
Guanxin Jiang, Lars Villemoes, Arijit Biswas
We show how a neural network can be trained on individual intrusive listening test scores to predict a distribution of scores for each pair of reference and coded input stereo or b…
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…
High Quality Audio Coding with MDCTNet
Grant Davidson, Mark Vinton, Per Ekstrand +3
We propose a neural audio generative model, MDCTNet, operating in the perceptually weighted domain of an adaptive modified discrete cosine transform (MDCT). The architecture of the…
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…