A novel iterative conditional maximization method for post-nonlinear underdetermined blind source separation

C. Wei*, L. C. Khor, W. L. Woo, S. S. Dlay

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

An iterative conditional maximization method originated from Bayesian statistics is proposed in this paper to offer a solution for blind source separation under a post-nonlinear underdetermined environment. The proposed algorithm estimate the sources and mixing matrix through their individual marginal probabilities instead of join probability. A Generalized Gaussian Distribution model is applied to approximate the prior information of probability distributions. The unknown nonlinear function is also estimated and modeled by a Multilayer Perceptron (MLP) neural network. All parameters are updated iteratively until convergence to a fixed state has been achieved. The proposed algorithm is tested on real audio wave and the performance is measured by modified Mean Square Error (MSE). The obtained results show that the proposed algorithm gains substantial improvements compared with the conventional linear algorithm.

Original languageEnglish
Title of host publication2007 15th International Conference on Digital Signal Processing, DSP 2007
PublisherIEEE
Pages551-554
Number of pages4
ISBN (Electronic)1424408822
ISBN (Print)1424408814
DOIs
Publication statusPublished - 13 Aug 2007
Event2007 15th International Conference onDigital Signal Processing, DSP 2007 - Wales, United Kingdom
Duration: 1 Jul 20074 Jul 2007

Conference

Conference2007 15th International Conference onDigital Signal Processing, DSP 2007
CountryUnited Kingdom
CityWales
Period1/07/074/07/07

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