BSS algorithm by diffusion mixing non-parametric density estimator
by Fasong Wang; Rui Li
International Journal of Modelling, Identification and Control (IJMIC), Vol. 15, No. 1, 2012

Abstract: Diffusion mixing estimator (DME)-based non-parametric blind source separation (BSS) algorithm is proposed under the framework of non-Bayesian framework and natural gradient optimisation method. In order to improve the performance of signal separation by BSS, the probability distribution of source signals must be described as accurately as possible. Compared to the non-parametric fixed-width kernel density estimator (FKDE) method, the DME with a new data-driven bandwidth selection method can improve the performance of FKDE, which is inspired via a Langevin diffusion process. Moreover, the direct estimation of the score functions can separate the hybrid mixtures of sources that contain both symmetric and asymmetric distribution source signals and do not need to assume the parametric non-linear functions as them. The effectiveness of the proposed algorithm has been confirmed by simulation experiments.

Online publication date: Sat, 29-Nov-2014

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