Underdetermined blind source separation based on continuous density hidden Markov models

Xiaoming Zhu, Keshab K Parhi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, a novel method is developed to solve the problem of underdetermined blind source separation, where the number of mixtures is smaller than that of sources. Generalized Gaussian Distributions (GGDs) are used to model the source signals and generative Continuous Density Hidden Markov Models (CDHMMs) are derived to track the nonstationarity inside the source signals. Each source signal can switch between several states such that the separation performance can be significantly improved. The model parameters are trained through the Expectation Maximization (EM) algorithm and the source signals are estimated via the Maximum a Posteriori (MAP) approach. Compared with the results of L1-norm solution, our proposed algorithm has obtained much better output signal-to-noise ratio (SNR) and the separation results are more realistic.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages4126-4129
Number of pages4
DOIs
StatePublished - Nov 8 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: Mar 14 2010Mar 19 2010

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CountryUnited States
CityDallas, TX
Period3/14/103/19/10

Keywords

  • Generalized Gaussian distribution
  • Hidden Markov model
  • Nonstationary signals
  • Underdetermined blind source separation

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  • Cite this

    Zhu, X., & Parhi, K. K. (2010). Underdetermined blind source separation based on continuous density hidden Markov models. In 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings (pp. 4126-4129). [5495730] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2010.5495730