Blind Separation of Convolutive Mixtures of Cyclostationary Sources Using an Extended Natural Gradient Method

Wang, W, Jafari, M, Sanei, S and Chambers, J (2003) Blind Separation of Convolutive Mixtures of Cyclostationary Sources Using an Extended Natural Gradient Method In: ISSPA 2003, 2003-07-01 - 2003-07-04, Paris, France.

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Abstract

An on-line adaptive blind source separation algorithm for the separation of convolutive mixtures of cyclostationary source signals is proposed. The algorithm is derived by applying natural gradient iterative learning to the novel cost function which is defined according to the wide sense cyclostationarity of signals. The efficiency of the algorithm is supported by simulations, which show that the proposed algorithm has improved performance for the separation of convolved cyclostationary signals in terms of convergence speed and waveform similarity measurement, as compared to the conventional natural gradient algorithm for convolutive mixtures.

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Divisions :	Surrey research (other units)
Authors :	Wang, W, Jafari, M, Sanei, S and Chambers, J
Date :	1 July 2003
DOI :	10.1109/ISSPA.2003.1224823
Contributors :	Contribution Name Email ORCID http://www.loc.gov/loc.terms/relators/PBL IEEE,
Depositing User :	Symplectic Elements
Date Deposited :	28 Mar 2017 14:43
Last Modified :	23 Jan 2020 12:49
URI:	http://epubs.surrey.ac.uk/id/eprint/596107

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