LEADER 06794nam 22008175 450 001 9910144349403321 005 20200705174926.0 010 $a3-540-30110-0 024 7 $a10.1007/b100528 035 $a(CKB)1000000000212556 035 $a(DE-He213)978-3-540-30110-3 035 $a(SSID)ssj0000178184 035 $a(PQKBManifestationID)11198908 035 $a(PQKBTitleCode)TC0000178184 035 $a(PQKBWorkID)10221314 035 $a(PQKB)11146506 035 $a(MiAaPQ)EBC3088136 035 $a(PPN)15517830X 035 $a(EXLCZ)991000000000212556 100 $a20121227d2004 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIndependent Component Analysis and Blind Signal Separation $eFifth International Conference, ICA 2004, Granada, Spain, September 22-24, 2004, Proceedings /$fedited by Carlos G. Puntonet, Alberto Prieto 205 $a1st ed. 2004. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2004. 215 $a1 online resource (XLVI, 1270 p.) 225 1 $aLecture Notes in Computer Science,$x0302-9743 ;$v3195 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-23056-4 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aTheory and Fundamentals -- Linear Mixture Models -- Convolutive Models -- Nonlinear ICA and BSS -- Speech Processing Applications -- Image Processing Applications -- Biomedical Applications -- Other Applications -- Invited Contributions. 330 $aIn many situations found both in Nature and in human-built systems, a set of mixed signals is observed (frequently also with noise), and it is of great scientific and technological relevance to be able to isolate or separate them so that the information in each of the signals can be utilized. Blind source separation (BSS) research is one of the more interesting emerging fields now a days in the field of signal processing. It deals with the algorithms that allow the recovery of the original sources from a set of mixtures only. The adjective ?blind? is applied because the purpose is to estimate the original sources without any a priori knowledge about either the sources or the mixing system. Most of the models employed in BSS assume the hypothesis about the independence of the original sources. Under this hypothesis,a BSS problem can be considered as a particular case of independent component analysis(ICA),a linear transformation technique that, starting from a multivariate representation of the data, minimizes the statistical dependence between the components of the representation. It can be claimed that most of the advances in ICA have been motivated by the search for solutions to the BSS problem and, the other way around,advances in ICA have been immediately applied to BSS. ICA and BSS algorithms start from a mixture model, whose parameters are estimated from the observed mixtures. Separation is achieved by applying the inverse mixture model to the observed signals(separating or unmixing model).Mixturem- els usually fall into three broad categories: instantaneous linear models, convolutive models and nonlinear models ,the ?rstone being the simplest but,in general,not near realistic applications. The development and test of the algorithms can be accomplished through synthetic data or with real-world data.Obviously, the most important aim(and most difficult) is the separation of real-world mixtures. BSS and ICA have strong relations also, apart from signal processing, with other fields such as statistics and artificial neural networks. As long as we can find a system that emits signals propagated through a mean, andthosesignalsarereceivedbyasetofsensorsandthereisaninterestinrecovering the original sources,we have a potential field of application for BSS and ICA. Inside that wide range of applications we can find, for instance: noise reduction applications, biomedical applications,audio systems,telecommunications,and many others. This volume comes out just 20 years after the first contributions in ICA and BSS 1 appeared . Therein after,the number of research groups working in ICA and BSS has been constantly growing, so that nowadays we can estimate that far more than 100 groups are researching in these fields. As proof of the recognition among the scientific community of ICA and BSS developments there have been numerous special sessions and special issues in several well- 1 J.Herault, B.Ans,?Circuits neuronaux à synapses modi?ables: décodage de messages composites para apprentissage non supervise?, C.R. de l?Académie des Sciences, vol. 299, no. III-13,pp.525?528,1984. 410 0$aLecture Notes in Computer Science,$x0302-9743 ;$v3195 606 $aMathematical analysis 606 $aAnalysis (Mathematics) 606 $aSpecial purpose computers 606 $aAlgorithms 606 $aComputers 606 $aCoding theory 606 $aInformation theory 606 $aStatistics  606 $aAnalysis$3https://scigraph.springernature.com/ontologies/product-market-codes/M12007 606 $aSpecial Purpose and Application-Based Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/I13030 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aComputation by Abstract Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/I16013 606 $aCoding and Information Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/I15041 606 $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008 615 0$aMathematical analysis. 615 0$aAnalysis (Mathematics). 615 0$aSpecial purpose computers. 615 0$aAlgorithms. 615 0$aComputers. 615 0$aCoding theory. 615 0$aInformation theory. 615 0$aStatistics . 615 14$aAnalysis. 615 24$aSpecial Purpose and Application-Based Systems. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aComputation by Abstract Devices. 615 24$aCoding and Information Theory. 615 24$aStatistics and Computing/Statistics Programs. 676 $a004.6 702 $aPuntonet$b Carlos G$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPrieto$b Alberto$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910144349403321 996 $aIndependent Component Analysis and Blind Signal Separation$9772269 997 $aUNINA