An effective method to improve convergence for sequential blind source separation

Authors:
L. Yuan;Enfang. Sang;W. Wang;J. A. Chambers
Affiliations:
Lianxi Yuan and Enfang Sang , Harbin Acoustic Engineering College, Harbin Engineering University, China;Lianxi Yuan and Enfang Sang , Harbin Acoustic Engineering College, Harbin Engineering University, China;W. Wang and J. A. Chambers, the Centre of Digital Signal Processing, Cardiff University, United Kingdom;W. Wang and J. A. Chambers, the Centre of Digital Signal Processing, Cardiff University, United Kingdom
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

Based on conventional natural gradient algorithm (NGA) and equivariant adaptive separation via independence algorithm (EASI), a novel sign algorithm for on-line blind separation of independent sources is presented. A sign operator for the adaptation of the separation model is obtained from the derivation of a generalized dynamic separation model. A variable step-size sign algorithm rooted in NGA is also derived to better match the dynamics of the input signals and unmixing matrix. The proposed algorithms are appealing in practice due to their computational simplicity. Experimental results verify the superior convergence performance over conventional NGA and EASI algorithm in both stationary and non-stationary environments.