A new approach to blind source separation with global optimal property

Yiu Ming CHEUNG*, Hailin Liu

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

14 Citations (Scopus)

Abstract

This paper presents a new independency metric for blind source separation (BSS) problem. It is mathematically proved that the metric value of any linear combination of source signals is less than the largest one of sources under a loose condition. Further, the global optimization of this new metric is achieved by formulating it as a generalized eigenvalue problem. Subsequently, we guarantee to find out a correct de-mixing matrix through maximizing the proposed metric to separate the sources. The simulation results have shown its success in separating the linear combinations of sub-Gaussian and super-Gaussian sources with at most one Gaussian signal.

Original languageEnglish
Pages137-141
Number of pages5
Publication statusPublished - 2004
EventProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence - Grindelwald, Switzerland
Duration: 23 Feb 200425 Feb 2004

Conference

ConferenceProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence
Country/TerritorySwitzerland
CityGrindelwald
Period23/02/0425/02/04

Scopus Subject Areas

  • Engineering(all)

User-Defined Keywords

  • Blind Source Separation
  • Generalized Eigenvalue Problem
  • Global Optimization
  • Independency Metric
  • Independent Component Analysis

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