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1、VariableStep-SizeSpeechBlindSeparationEmployingLaplacianNormalMixtureDistributionModelZhenhuaZhi,XueyingZhangJianfenMaDepartmentofInformationEngineeringDepartmentofComputing&SoftwareEngineeringTaiyuanUniversityofTechnologyTaiyuanUniversityofTechnologyTaiyuan,ChinaTaiyuan,Chinazhizhenhua@sina.c
2、om;tyzhangxy@163.commajianfen@eyou.comAbstract—Fortheblindsourceseparationalgorithm,choicesofSimulationsofthealgorithm’sperformancearedescribedinnonlinearfunctionandstep-sizeareveryimportant.Inthissection5.Finally,insection6wegiveourconclusions.paper,weproposeanovelnaturalgradientspeechblindse
3、parationalgorithm.ThemainingredientsaretheuseofaII.NATURALGRADIENTBLINDSOURCEvariablestep-sizetechnologyandanonlinearfunctionbasedonSEPARATIONALGORITHMLaplaciannormalmixturedistribution.SimulationresultsindicatetheproposedmethodensuressteadystateerrorofThegoaloftheBSStechniqueistofindaseparati
4、ngalgorithmandacceleratesconvergencespeedofalgorithm.matrixWthatperformstheinverseoperationofthemixingprocess,assubsequentlyusedintheseparationmodel.Figure1showsablockdiagramofthemodel.I.INTRODUCTIONBlindsourceseparation(BSS)technology[1]isanewresearchfieldinlastdecadeofthe20thcentury,isthecor
5、es(k)x(k)y(k)contentinblindsignalprocessingresearchfield,andisalsoacombinationproductofartificialneuralnetworkandstatisticalAWsignalprocessingandinformationtheory.MostformulationstotheBSStaskassumethatasetofsensorsignalscontainlinearmixturesofseveralsourcesignalsofinterest.Thegoalofblindsource
6、separationistorecovertheoriginalsourcesFigure1.BlockdiagramofthebasicBSSgivenonlytheobservedmixtures.Infigure1,Aisanunknown,fullcolumnrankmixingTTTInthenaturalgradientblindsourceseparationalgorithmmatrix.s[s1,...,sn],x[x1,...,xm],y[y1,...,ym].In[2],nonlinearfunctionandstep-sizearetwoimporta
7、ntfactorsthispaper,wesetmn.whichaffectalgorithmperformance.Inordertoimprovealgorithmperformance,wemustselectappropriatenonlinearLetusconsiderthelinearnetworkofthefigure1whosefunction[3][4]andusevariablestep-sizetechnology[5][6].outputy