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Signal Separation of Nonlinear Time-Delayed Mixture: Time Domain Approach

Lookup NU author(s): Dr Wai Lok Woo, Professor Satnam Dlay, Professor John Hudson



In this paper, a novel algorithm is proposed to solve blind signal separation of nonlinear time-delayed mixtures of statistically independent sources. Both mixing and nonlinear distortion are included in the proposed model. Maximum Likelihood (ML) approach is developed to estimate the parameters in the model and this is formulated within the framework of the generalized Expectation-Maximization (EM) algorithm. Adaptive polynomial basis expansion is used to estimate the nonlinearity of the mixing model. In the E-step, the sufficient statistics associated with the source signals are estimated while in the M-step, the parameters are optimized by using these statistics. Generally, the nonlinear distortion renders the statistics intractable and difficult to be formulated in a closed form. However, in this paper it is proved that with the use of Extended Kalman Smoother (EKS) around a linearized point, the M-step is made tract able and can be solved by linear equations.

Publication metadata

Author(s): Woo WL, Dlay SS, Hudson JE

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: International Conference on Signal Acquisition and Processing (ICSAP 2009)

Year of Conference: 2009

Pages: 203-207

Publisher: IEEE Computer Society


DOI: 10.1109/ICSAP.2009.11

Library holdings: Search Newcastle University Library for this item

ISBN: 9780769535944