Blind separation of sources that have spatiotemporal variance dependencies

Authors:
Aapo Hyvärinen;Jarmo Hurri
Affiliations:
Neural Networks Research Centre, Helsinki University of Technology, Finland and Basic Research Unit, Helsinki Institute for Information Technology, Department of Computer Science, University of He ...;Neural Networks Research Centre, Helsinki University of Technology, Finland
Venue:
Signal Processing - Special issue on independent components analysis and beyond
Year:
2004

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Abstract

In blind source separation methods, the sources are typically assumed to be independent. Some methods are also able to separate dependent sources by estimating or assuming a parametric model for their dependencies. Here, we propose a method that separates dependent sources without a parametric model of their dependency structure. This is possible by introducing some general assumptions on the structure of the dependencies: the sources are dependent only through their variances (general activity levels), and the variances of the sources have temporal correlations. The method can be called double-blind because of this additional blind aspect: We do not need to estimate (or assume) a parametric model of the dependencies, which is in stark contrast to most previous methods.