Extraction of statistically dependent sources with temporal structure

A. K. Barros, A. Cichocki, N. Ohnishi

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

2 Citations (Scopus)

Abstract

In this work we develop a very simple batch learning algorithm for semi-blind extraction of a desired source signal with temporal structure from linear mixtures. Although we use the concept of sequential blind extraction of sources and independent component analysis (ICA), we do not carry out the extraction in a completely blind manner neither we assume that sources are statistically independent. In fact, we show that the a priori information about the autocorrelation function of primary sources can be used to extract the desired signals (sources of interest) from their mixtures. Extensive computer simulations and real data application experiments confirm the validity and high performance of the proposed algorithm.

Original languageEnglish
Title of host publicationProceedings - 6th Brazilian Symposium on Neural Networks, SBRN 2000
EditorsCarlos H.C. Ribeiro, Felipe M.G. Franca
PublisherIEEE Computer Society
Pages61-65
Number of pages5
ISBN (Electronic)0769508561
DOIs
Publication statusPublished - 2000
Externally publishedYes
Event6th Brazilian Symposium on Neural Networks, SBRN 2000 - Rio de Janeiro, Brazil
Duration: 22 Nov 200025 Nov 2000

Publication series

NameProceedings - Brazilian Symposium on Neural Networks, SBRN
Volume2000-January
ISSN (Print)1522-4899

Conference

Conference6th Brazilian Symposium on Neural Networks, SBRN 2000
Country/TerritoryBrazil
CityRio de Janeiro
Period22/11/0025/11/00

Keywords

  • Application software
  • Autocorrelation
  • Computer simulation
  • Data mining
  • Decorrelation
  • Independent component analysis
  • Magnetic sensors
  • Signal processing algorithms
  • Source separation
  • Speech

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