MIMO-AR system identification and blind source separation for GMM-distributed sources

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18 Scopus citations

Abstract

The problem of blind source separation (BSS) and system identification for multiple-input multiple-output (MIMO) auto-regressive (AR) mixtures is addressed in this paper. Two new time-domain algorithms for system identification and BSS are proposed based on the Gaussian mixture model (GMM) for sources distribution. Both algorithms are based on the generalized expectation-maximization (GEM) method for joint estimation of the MIMO-AR model parameters and the GMM parameters of the sources. The first algorithm is derived under the assumption of unstructured input signal statistics, while the second algorithm incorporates the prior knowledge about the structure of the input signal statistics due to the statistically independent source assumption. These methods are tested via simulations using synthetic and audio signals. The system identification performances are tested by comparison between the state transition matrix estimation using the proposed algorithms and the well-known multidimensional Yule-Walker solution followed by an instantaneous BSS method. The results show that the proposed algorithms outperform the Yule-Walker based approach. The BSS performances were compared to other convolutive BSS methods. The results show that the proposed algorithms achieve higher signal-to-interference ratio (SIR) compared to the other tested methods.

Original languageEnglish
Pages (from-to)1717-1730
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume57
Issue number5
DOIs
StatePublished - 25 May 2009

Keywords

  • Blind deconvolution
  • Blind source separation (BSS)
  • Convolutive mixtures
  • Gaussian mixture model (GMM)
  • Generalized expectation-maximization (GEM)
  • MIMO system identification
  • Maximum- likelihood
  • Multidimensional AR
  • Multiple-input multiple-output autoregressive (MIMO-AR)

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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