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1、AutomaticMusicalGenreClassificationOfAudioSignalsGeorgeTzanetakisGeorgEsslPerryCookComputerScienceDepartmentComputerScienceDep.ComputerScienceandMusicDep.35OldenStreet35OldenStreet35OldenStreetPrincetonNJ08544PrincetonNJ08544PrincetonNJ08544+16092585030+16092585030+16092585030gtzan@cs.pr
2、inceton.edugessl@cs.princeton.eduprc@cs.princeton.eduABSTRACTMusicalgenresarecategoricaldescriptionsthatareusedtoInthiswork,algorithmsforautomaticgenreclassificationaredescribemusic.Theyarecommonlyusedtostructuretheexplored.Asetoffeaturesforrepresentingthemusicsurfaceandincreasingamounts
3、ofmusicavailableindigitalformontherhythmicstructureofaudiosignalsisproposed.TheperformanceWebandareimportantformusicinformationretrieval.ofthisfeaturesetisevaluatedbytrainingstatisticalpatternGenrecategorizationforaudiohastraditionallybeenrecognitionclassifiersusingaudiocollectionscollec
4、tedfromcompactdisks,radioandtheweb.Audiosignalscanbeperformedmanually.Aparticularmusicalgenreisautomaticallyclassifiedusingahierarchyofgenresthatcanbecharacterizedbystatisticalpropertiesrelatedtotherepresentedasatreewith15nodes.Basedonthisautomaticgenreinstrumentation,rhythmicstructurean
5、dformofitsclassificationandtheextractedfeaturestwographicalusermembers.Inthiswork,algorithmsfortheautomaticgenreinterfacesforbrowsingandinteractingwithlargeaudiocategorizationofaudiosignalsaredescribed.Morecollectionshavebeendeveloped.specifically,weproposeasetoffeaturesforrepresentingte
6、xtureandinstrumentation.Inadditionanovelsetof2.RELATEDWORKfeaturesforrepresentingrhythmicstructureandstrengthisAnearlyoverviewofaudioinformationretrieval(AIR)isgiveninproposed.Theperformanceofthosefeaturesetshasbeen[3].Statisticalpatternrecognitionbasedonspectralfeatureshasevaluatedbytra
7、iningstatisticalpatternrecognitionbeenusedtoclassifyMusicvsSpeech[4],Isolatedsounds[5,6]classifiersusingrealworldaudiocollections.BasedontheandInstruments[7].Extractionofpsychoacousticfeaturesrelatedautomatichierarchicalgenreclassificationtwographicaltomusicsurfaceandthei