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Erschienen in:
2005 | OriginalPaper | Buchkapitel
An Effective Method to Improve Convergence for Sequential Blind Source Separation
verfasst von : L. Yuan, Enfang. Sang, W. Wang, J. A. Chambers
Erschienen in: Advances in Natural Computation
Verlag: Springer Berlin Heidelberg
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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.