Blind Separation of Filtered Sources Using State-Space Approach
Abstract
In this paper we present a novel approach to multichannel blind separation/generalized deconvolution, assuming that both mixing and demixing models are described by stable linear state-space sys(cid:173) tems. We decompose the blind separation problem into two pro(cid:173) cess: separation and state estimation. Based on the minimization of Kullback-Leibler Divergence, we develop a novel learning algo(cid:173) rithm to train the matrices in the output equation. To estimate the state of the demixing model, we introduce a new concept, called hidden innovation, to numerically implement the Kalman filter. Computer simulations are given to show the validity and high ef(cid:173) fectiveness of the state-space approach.
Cite
Text
Zhang and Cichocki. "Blind Separation of Filtered Sources Using State-Space Approach." Neural Information Processing Systems, 1998.Markdown
[Zhang and Cichocki. "Blind Separation of Filtered Sources Using State-Space Approach." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/zhang1998neurips-blind/)BibTeX
@inproceedings{zhang1998neurips-blind,
title = {{Blind Separation of Filtered Sources Using State-Space Approach}},
author = {Zhang, Liqing and Cichocki, Andrzej},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {648-656},
url = {https://mlanthology.org/neurips/1998/zhang1998neurips-blind/}
}