Space or time adaptive signal processing by neural network models
AIP Conference Proceedings 151 on Neural Networks for Computing
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Complex FastIVA: a robust maximum likelihood approach of MICA for convolutive BSS
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Blind Source Separation Exploiting Higher-Order Frequency Dependencies
IEEE Transactions on Audio, Speech, and Language Processing
A Uniform Framework for Ad-Hoc Indexes to Answer Reachability Queries on Large Graphs
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
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Proportional variance dependency among the frequency components is characteristic of natural signals and has been utilized in frequency-domain blind source separation to solve the permutation problem. In order to increase robustness in such methods, overall measures have been preferred to the measures between directly neighboring frequency components. The overall variance dependency pattern in the fullband, however, can vary by signals and is difficult to be modeled, whereas in smaller subbands the proportional variance dependency is more definite. Here, a novel permutation correction method that utilizes the proportional variance dependency in small subbands is proposed. A windowed likelihood function that uses source priors with internal variance dependency is employed as the measure of permutation correction. This method not only shows robust separation performance but also is computation-wise very efficient.