Source Separation with a Sensor Array Using Graphical Models and Subband Filtering
Abstract
Source separation is an important problem at the intersection of several fields, including machine learning, signal processing, and speech tech- nology. Here we describe new separation algorithms which are based on probabilistic graphical models with latent variables. In contrast with existing methods, these algorithms exploit detailed models to describe source properties. They also use subband filtering ideas to model the reverberant environment, and employ an explicit model for background and sensor noise. We leverage variational techniques to keep the compu- tational complexity per EM iteration linear in the number of frames.
Cite
Text
Attias. "Source Separation with a Sensor Array Using Graphical Models and Subband Filtering." Neural Information Processing Systems, 2002.Markdown
[Attias. "Source Separation with a Sensor Array Using Graphical Models and Subband Filtering." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/attias2002neurips-source/)BibTeX
@inproceedings{attias2002neurips-source,
title = {{Source Separation with a Sensor Array Using Graphical Models and Subband Filtering}},
author = {Attias, Hagai},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {1229-1236},
url = {https://mlanthology.org/neurips/2002/attias2002neurips-source/}
}