Online Single-Microphone Source Separation Using Non-Linear Autoregressive Models
Abstract
In this paper a modular approach to single-microphone source separation is proposed. A probabilistic model for mixtures of observations is constructed, where the independent underlying source signals are described by non-linear autoregressive models. Source separation in this model is achieved by performing online probabilistic inference through an efficient message passing procedure. For retaining tractability with the non-linear autoregressive models, three different approximation methods are described. A set of experiments shows the effectiveness of the proposed source separation approach. The source separation performance of the different approximation methods is quantified through a set of verification experiments. Our approach is validated in a speech denoising task.
Cite
Text
Erp and Vries. "Online Single-Microphone Source Separation Using Non-Linear Autoregressive Models." Proceedings of The 11th International Conference on Probabilistic Graphical Models, 2022.Markdown
[Erp and Vries. "Online Single-Microphone Source Separation Using Non-Linear Autoregressive Models." Proceedings of The 11th International Conference on Probabilistic Graphical Models, 2022.](https://mlanthology.org/pgm/2022/erp2022pgm-online/)BibTeX
@inproceedings{erp2022pgm-online,
title = {{Online Single-Microphone Source Separation Using Non-Linear Autoregressive Models}},
author = {Erp, Bart and Vries, Bert},
booktitle = {Proceedings of The 11th International Conference on Probabilistic Graphical Models},
year = {2022},
pages = {37-48},
volume = {186},
url = {https://mlanthology.org/pgm/2022/erp2022pgm-online/}
}