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Description
  • Slepá separace lineárních směsí nezávislých signálů je problémem který se vyskytuje v biomedcínslých aplikacích i ve zpracování akustických signálů. Algoritmus EFICA poskytuje asymptoticky optimální řešení tohoto problému, pokud všechny zdroje mají zobecněné Gaussovo rozložení, jsou nezávislé s tejně rozložené v čase. Algoritmus WASOBI je asymptoticky optimální, pokud se všechny zdroje dají popsat jako autoregresní náhodné procesy s Gaussovým rozložením. Signály objevující se v praxi mají zpravidla jak ne-Gaussovské rozložení, tak netriviální časovou korelační strukturu. V článku navrhujeme algoritmus, který kombinuje silné stránky zmíněných algoritmů při separaci takových směsí. V simulacích jsou ukázány vynikající vlastnosti navrženého řešení v porovnání s dalšími existujícími algoritmy. (cs)
  • Blind inversion of a linear and instantaneous mixture of source signals is a problem often encountered in many signal processing applications. Efficient FastICA (EFICA) offers an asymptotically optimal solution to this problem when all of the sources obey a generalized Gaussian distribution, at most one of them is Gaussian, and each is independent and identically distributed in time. Likewise, Weights-Adjusted Second Order Blind Identification (WASOBI) is asymptotically optimal when all the sources are Gaussian and can be modeled as Autoregressive (AR) processes with distinct spectra. Nevertheless, real-life mixtures are likely to contain both Gaussian AR and non-Gaussian iid sources, rendering WASOBI and EFICA severely sub-optimal. In this paper we propose a novel scheme for combining the strengths of EFICA and WASOBI in order to deal with such hybrid mixtures. Simulations show that our approach outperforms competing algorithms designed for separating similar mixtures.
  • Blind inversion of a linear and instantaneous mixture of source signals is a problem often encountered in many signal processing applications. Efficient FastICA (EFICA) offers an asymptotically optimal solution to this problem when all of the sources obey a generalized Gaussian distribution, at most one of them is Gaussian, and each is independent and identically distributed in time. Likewise, Weights-Adjusted Second Order Blind Identification (WASOBI) is asymptotically optimal when all the sources are Gaussian and can be modeled as Autoregressive (AR) processes with distinct spectra. Nevertheless, real-life mixtures are likely to contain both Gaussian AR and non-Gaussian iid sources, rendering WASOBI and EFICA severely sub-optimal. In this paper we propose a novel scheme for combining the strengths of EFICA and WASOBI in order to deal with such hybrid mixtures. Simulations show that our approach outperforms competing algorithms designed for separating similar mixtures. (en)
Title
  • A Hybrid Technique for Blind Separation of Non-Gaussian and Time-Correlated Sources Using a Multicomponent Approach
  • Hybridní technika slepé separace negaussovských a časově korelovaných zdrojů s využitím vícenásobných komponent (cs)
  • A Hybrid Technique for Blind Separation of Non-Gaussian and Time-Correlated Sources Using a Multicomponent Approach (en)
skos:prefLabel
  • A Hybrid Technique for Blind Separation of Non-Gaussian and Time-Correlated Sources Using a Multicomponent Approach
  • Hybridní technika slepé separace negaussovských a časově korelovaných zdrojů s využitím vícenásobných komponent (cs)
  • A Hybrid Technique for Blind Separation of Non-Gaussian and Time-Correlated Sources Using a Multicomponent Approach (en)
skos:notation
  • RIV/67985556:_____/08:00306563!RIV08-AV0-67985556
http://linked.open.../vavai/riv/strany
  • 421;430
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1M0572), P(GP102/07/P384), Z(AV0Z10750506)
http://linked.open...iv/cisloPeriodika
  • 3
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 354214
http://linked.open...ai/riv/idVysledku
  • RIV/67985556:_____/08:00306563
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • blind source separation; independent component analysis (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • US - Spojené státy americké
http://linked.open...ontrolniKodProRIV
  • [7D243C52285E]
http://linked.open...i/riv/nazevZdroje
  • IEEE Transactions on Neural Networks
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 19
http://linked.open...iv/tvurceVysledku
  • Koldovský, Zbyněk
  • Tichavský, Petr
  • Yeredor, A.
  • Doron, E.
  • Gómez-Herrero, G.
http://linked.open...n/vavai/riv/zamer
issn
  • 1045-9227
number of pages
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