Blind source separation for MIMO-AR mixtures using GMM

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1 Scopus citations

Abstract

The problem of blind source separation (BSS) of multiple-input multiple-output (MIMO) autoregressive (AR) mixture is addressed in this paper. A new time-domain method for system identification and BSS for MIMO-AR models in proposed based on the Gaussian mixture model (GMM) for sources distribution. The algorithm is based on generalized expectation-maximization (GEM) for joint estimation of the AR model parameters and the GMM parameters of the sources. The method is tested via simulations of synthetic and audio signals mixed by a MIMO-AR model. The results show that the proposed algorithm outperforms the well-known multidimensional linear predictive coding (LPC), and it enables to achieve higher signal-to-interference ratio (SIR) in the BSS problem.

Original languageEnglish
Title of host publication2006 IEEE 24th Convention of Electrical and Electronics Engineers in Israel, IEEEI
Pages310-314
Number of pages5
DOIs
StatePublished - 1 Dec 2006
Event2006 IEEE 24th Convention of Electrical and Electronics Engineers in Israel, IEEEI - Eilat, Israel
Duration: 15 Nov 200617 Nov 2006

Publication series

NameIEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings

Conference

Conference2006 IEEE 24th Convention of Electrical and Electronics Engineers in Israel, IEEEI
Country/TerritoryIsrael
CityEilat
Period15/11/0617/11/06

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

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