Problems Using Deep Generative Models for Probabilistic Audio Source Separation
Abstract
Recent advancements in deep generative modeling make it possible to learn prior distributions from complex data that subsequently can be used for Bayesian inference. However, we find that distributions learned by deep generative models for audio signals do not exhibit the right properties that are necessary for tasks like audio source separation using a probabilistic approach. We observe that the learned prior distributions are either discriminative and extremely peaked or smooth and non-discriminative. We quantify this behavior for two types of deep generative models on two audio datasets.
Cite
Text
Frank and Ilse. "Problems Using Deep Generative Models for Probabilistic Audio Source Separation." NeurIPS 2020 Workshops: ICBINB, 2020.Markdown
[Frank and Ilse. "Problems Using Deep Generative Models for Probabilistic Audio Source Separation." NeurIPS 2020 Workshops: ICBINB, 2020.](https://mlanthology.org/neuripsw/2020/frank2020neuripsw-problems/)BibTeX
@inproceedings{frank2020neuripsw-problems,
title = {{Problems Using Deep Generative Models for Probabilistic Audio Source Separation}},
author = {Frank, Maurice and Ilse, Maximilian},
booktitle = {NeurIPS 2020 Workshops: ICBINB},
year = {2020},
url = {https://mlanthology.org/neuripsw/2020/frank2020neuripsw-problems/}
}