7 citations
6 papers
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…
Multichannel-based learning for audio object extraction
Daniel Arteaga, Jordi Pons
The current paradigm for creating and deploying immersive audio content is based on audio objects, which are composed of an audio track and position metadata. While rendering an ob…
On permutation invariant training for speech source separation
Xiaoyu Liu, Jordi Pons
We study permutation invariant training (PIT), which targets at the permutation ambiguity problem for speaker independent source separation models. We extend two state-of-the-art P…
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.…
Exact multiplicative updates for convolutional -NMF in 2D
Pedro J. Villasana T., Stanislaw Gorlow
In this paper, we extend the -CNMF to two dimensions and derive exact multiplicative updates for its factors. The new updates generalize and correct the nonnegative matrix facto…
Multiplicative Updates for Convolutional NMF Under -Divergence
Pedro J. Villasana T., Stanislaw Gorlow, Arvind T. Hariraman
In this letter, we generalize the convolutional NMF by taking the -divergence as the contrast function and present the correct multiplicative updates for its factors in closed f…