Hidden Markov blind source separation a of post-nonlinear mixture

Jingyi Zhang*, W. L. Woo, S. S. Dlay

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

In this paper, a novel solution is developed to solve the problem of separating noisy and post-nonlinearly distorted mixture. In the proposed work, the source signals are nonstationary and temporally correlated. A generative model based on Hidden Markov Model (HMM) is derived to track the nonstationarity of the source signal while the source signal itself is modeled by temporally correlated Generalized Gaussian Distribution (GGD) Model. The Maximum Likelihood (ML) approach is developed to estimate the parameters of the proposed model by using the Expectation Maximization (EM) algorithm and the source signals are estimated by Maximum a Posteriori (MAP) approach. The strength of the proposed approach lies in the tracking of the nonstationarity of the source signal by HMM and the temporal correlation by the autoregressive (AR) source model. This has resulted in high performance accuracy, fast convergence and efficient implementation of the estimation algorithm. Simulations have been investigated to verify the effectiveness of the proposed algorithm and the results have shown significant improvement has been obtained when compared with nonlinear algorithm without using HMM.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
PublisherIEEE
Pages1929-1932
Number of pages4
ISBN (Electronic)9781424414840
ISBN (Print)9781424414833
DOIs
Publication statusPublished - 12 May 2008
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: 31 Mar 20084 Apr 2008

Conference

Conference2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
CountryUnited States
CityLas Vegas, NV
Period31/03/084/04/08

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