Online Adaptive Quasi-Maximum Likelihood Blind Source Separation of Stationary Sources

Amir Weiss, Arie Yeredor

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

1 Scopus citations

Abstract

In the context of Blind Source Separation (BSS), we consider the problem of online separation of stationary sources. Based on the Maximum Likelihood (ML) solution for semi-blind separation of temporally-diverse Gaussian sources, and assuming that a parametric model of the sources' spectra is available, we propose an online adaptive Quasi-ML (QML) separation algorithm. The algorithm operates in an alternating fashion, updating at each iteration the (nuisance) spectra-characterizing parameters first, and then the demixing-matrix estimates, according to simple, computationally efficient update expressions which we derive. Our proposed algorithm, which leads to consistent separation of the sources, is demonstrated here, both analytically and empirically in a simulation experiment, for first-order autoregressive sources.

Original languageEnglish
Title of host publication2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538663783
DOIs
StatePublished - 2 Jul 2018
Externally publishedYes
Event2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018 - Eilat, Israel
Duration: 12 Dec 201814 Dec 2018

Publication series

Name2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018

Conference

Conference2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018
Country/TerritoryIsrael
CityEilat
Period12/12/1814/12/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Blind source separation
  • SeDJoCo
  • independent component analysis
  • quasi-maximum likelihood

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