LEADER 03831nam 2200673 450 001 996466247003316 005 20220514103722.0 010 $a3-540-74494-0 024 7 $a10.1007/978-3-540-74494-8 035 $a(CKB)1000000000490665 035 $a(SSID)ssj0000318224 035 $a(PQKBManifestationID)11244237 035 $a(PQKBTitleCode)TC0000318224 035 $a(PQKBWorkID)10308550 035 $a(PQKB)11717280 035 $a(DE-He213)978-3-540-74494-8 035 $a(MiAaPQ)EBC3062121 035 $a(MiAaPQ)EBC6711209 035 $a(Au-PeEL)EBL6711209 035 $a(OCoLC)190861936 035 $a(PPN)123731585 035 $a(EXLCZ)991000000000490665 100 $a20220514d2007 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 00$aIndependent component analysis and signal separation $e7th international conference, ICA 2007, London, UK, September 9-12, 2007, proceedings /$fMike E. Davies [and three others] (editors) 205 $a1st ed. 2007. 210 1$aBerlin ;$aHeidelberg :$cSpringer,$d[2007] 210 4$dİ2007 215 $a1 online resource (XIX, 847 p.) 225 1 $aLecture notes in computer science ;$v4666 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-74493-2 320 $aIncludes bibliographical references and index. 327 $aTheory -- Algorithms -- Sparse Methods -- Speech and Audio Applications -- Biomedical Applications -- Miscellaneous -- Keynote Talk. 330 $aThis volume contains the papers presented at the 7th International Conference on Independent Component Analysis (ICA) and Source Separation held in L- don, 9?12 September 2007, at Queen Mary, University of London. Independent Component Analysis and Signal Separation is one of the most exciting current areas of research in statistical signal processing and unsup- vised machine learning. The area has received attention from several research communities including machine learning, neural networks, statistical signal p- cessing and Bayesian modeling. Independent Component Analysis and Signal Separation has applications at the intersection of many science and engineering disciplinesconcernedwithunderstandingandextractingusefulinformationfrom data as diverse as neuronal activity and brain images, bioinformatics, com- nications, the World Wide Web, audio, video, sensor signals, or time series. This year?s event was organized by the EPSRC-funded UK ICA Research Network (www.icarn.org). There was also a minor change to the conference title this year with the exclusion of the word?blind?. The motivation for this was the increasing number of interesting submissions using non-blind or semi-blind techniques that did not really warrant this label. Evidence of the continued interest in the ?eld was demonstrated by the healthy number of submissions received, and of the 149 papers submitted just over two thirds were accepted. 410 0$aLecture notes in computer science. 606 $aSignal processing$xDigital techniques$vCongresses 606 $aBlind source separation$vCongresses 606 $aNeural networks (Computer science)$vCongresses 606 $aElectronic noise$vCongresses 606 $aIndependent component analysis$vCongresses 615 0$aSignal processing$xDigital techniques 615 0$aBlind source separation 615 0$aNeural networks (Computer science) 615 0$aElectronic noise 615 0$aIndependent component analysis 676 $a621.3822 702 $aDavies$b Mike$cDr., 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466247003316 996 $aIndependent Component Analysis and Signal Separation$9774136 997 $aUNISA