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The coherence function in blind source separation of convolutive mixtures of non-stationary signals

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2 Author(s)
Fancourt, C.L. ; Adaptive Image & Signal Process. Group, Sarnoff Corp, Princeton, NJ, USA ; Parra, L.

We propose a novel performance criteria and update mechanism for the blind decorrelation of an array of sensor measurements into independent sources, assuming each sensor measures a different convolutive mixture of statistically independent non-stationary sources. Specifically, the criteria is the sum of the magnitude squared coherence functions between all possible distinct pairs of outputs produced by a matrix of adaptable filters operating on the sensor measurements in the frequency domain. We then derive an efficient overlap-save online update equation based on stochastic gradient descent and recursive estimation of the coherence functions. We demonstrate separation within fractions of a second and convergence within a few seconds on real room recordings. We attribute this speed to the normalization and recursive estimates of the coherence functions

Published in:

Neural Networks for Signal Processing XI, 2001. Proceedings of the 2001 IEEE Signal Processing Society Workshop

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