Optimal pairing of signal components separated by blind techniques

In this letter, the problem of optimal pairing of signal components separated by blind techniques in different time-windows or in different frequency bins is addressed. The optimum pairing is defined as the one which minimizes the sum of some distances (criteria of dissimilarity) of the to-be-assign...

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Bibliographic Details
Published in:IEEE signal processing letters 2004-02, Vol.11 (2), p.119-122
Main Authors: Tichavsky, P., Koldovsky, Z.
Format: Article
Language:English
Subjects:
Algorithms
Bins
Biomedical signal processing
Blinds
Criteria
Evolution
Frequency estimation
Graph theory
Independent component analysis
Information theory
Optimal matching
Optimization
Signal processing
Signal processing algorithms
Source separation
Speech recognition
Vectors
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Summary:In this letter, the problem of optimal pairing of signal components separated by blind techniques in different time-windows or in different frequency bins is addressed. The optimum pairing is defined as the one which minimizes the sum of some distances (criteria of dissimilarity) of the to-be-assigned signal components. It is shown that the optimal pairing can be achieved by the Kuhn-Munkres algorithm known in graph theory as a solution to the optimal assignment problem. An advantage of the proposed pairing method is shown on data from electroencephalogram, which are blindly separated using the FastICA algorithm in a sliding time-window with the aim to study the time evolution of elements of the estimated mixing matrix.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2003.821658