NDLI: A learning algorithm with adaptive exponential stepsize for blind source separation of convolutive mixtures with reverberations

Content Provider	IEEE Xplore Digital Library
Author	Nakayama, K. Hirano, A. Horita, A.
Copyright Year	2003
Description	Author affiliation: Dept. of Inf. & Syst. Eng., Kanazawa Univ., Japan (Nakayama, K.; Hirano, A.; Horita, A.)
Abstract	First, convergence properties in blind source separation (BSS) of convolutive mixtures are analyzed. A fully recurrent network is taken into account. Convergence is highly dependent on relation among signal source power, transmission gain and delay in a mixing process. Especially, reverberation degrade separation performance. Second, a learning algorithm is proposed for this situation. In an unmixing block, feedback paths have an FIR filter. The filter coefficients are updated through the gradient algorithm starting from zero initial guess. The correction is exponentially scaled along the tap number. In other words, stepsize is exponentially weighted. Since the filter coefficients with a long delay are easily affected by the reverberations, their correction is suppressed. Exponential weighting is automatically adjusted by approximating an envelop of the filter coefficients in a learning process. Through simulation, good separation of performance, which is the same as in no reverberations condition, can be achieved by the proposed method.
Sponsorship	Int. Neural Network Soc. IEEE Neural Networks Soc
Starting Page	1092
Ending Page	1097
File Size	306982
Page Count	6
File Format	PDF
ISBN	0780378989
ISSN	10987576
DOI	10.1109/IJCNN.2003.1223843
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Blind source separation Reverberation Finite impulse response filter Source separation Signal processing algorithms Convergence Signal processing Feedback Delay IIR filters
Content Type	Text
Resource Type	Article

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