NDLI: An adaptive short-time frequency domain algorithm for blind separation of nonstationary convolved mixtures

Content Provider	IEEE Xplore Digital Library
Author	Kopriva, I. Devcic, Z. Szu, H.
Copyright Year	2001
Description	Author affiliation: Digital Media RF Lab., George Washington Univ., Washington, DC, USA (Kopriva, I.)
Abstract	We present a frequency domain algorithm derived for windowed-adaptive blind separation of convolved sources. Signal separation (filtering) is performed in short-time-windowed-frequency domain in terms of a finite filter length L obtaining faster convergence and better performance compared with the strictly time domain algorithms. In order to avoid the whitening effect the recurrent neural network, similar to the one proposed by Back and Tsoi (1994), is employed. A statistical independence test is done in time domain in order to determine the relative time-varying effect and solve the permutation indeterminacy problem. Corrections of the learning rules are introduced, which show to improve separation performance significantly. Additionally, the results developed by Amari et al. (2000) for the instantaneous mixtures are applied making learning equations computationally more efficient. To resolve the permutation problems the neural network outputs algorithm developed by Markowitz and Szu (1999) is applied.
Sponsorship	Int. Neural Network Soc.
Starting Page	424
Ending Page	429
File Size	614467
Page Count	6
File Format	PDF
ISBN	0780370449
ISSN	10987576
DOI	10.1109/IJCNN.2001.939057
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Frequency domain analysis Filters Signal processing algorithms Vectors Source separation Noise reduction Laser radar Radar tracking Optical noise Biomedical measurements
Content Type	Text
Resource Type	Article

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