改进神经网络的电子音乐辨识研究-豆柴文库

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改进神经网络的电子音乐辨识研究
Title:EnhancingElectronicMusicIdentificationusingDeepNeuralNetworks
Abstract:
Musicidentificationisafundamentaltaskindigitalaudioprocessingandhasbecomeparticularlychallengingintheeraofelectronicmusic,whichoftenlackstraditionalmusicalstructuresandincorporatescomplexsoundsynthesizersandeffects.Thispaperaimstoexplorehowdeepneuralnetworks(DNNs)canbeusedtoimprovetheaccuracyandefficiencyofelectronicmusicidentification.WeinvestigatevariousarchitecturesandtrainingtechniquestoenhancetheperformanceofDNNmodels,andpresentanempiricalevaluationoftheireffectiveness.TheresultsdemonstratethepotentialofDNNsinadvancingelectronicmusicidentificationresearch.
1.Introduction
Electronicmusicischaracterizedbyitswiderangeofsoundsandintricateaudioprocessingtechniques,makingitdifficulttoidentifyandcategorizeaccurately.Traditionalmusicidentificationmethodsrelyheavilyonmanualfeatureextraction,whichcanbetime-consumingandpronetoerrors.Theemergenceofdeeplearningtechniques,specificallyDNNs,hasrevolutionizedthefieldbyautomatingfeatureextractionandmodelingcomplexrelationshipswithinthedata.Inthispaper,wediscussthepotentialofDNNsinimprovingelectronicmusicidentification.
2.LiteratureReview
Wereviewexistingresearchonelectronicmusicidentification,highlightingthelimitationsoftraditionaltechniquesandintroducingrecentadvancementsindeeplearning.WediscussrelevantstudiesthathaveutilizedDNNsforaudioprocessingtasks,suchasmusicgenreclassificationandmusicrecognition,anddrawinspirationfromtheirmethodologiesandresults.
3.Methodology
WeoutlineourproposedmethodologyforelectronicmusicidentificationusingDNNs.ThisincludesselectinganappropriateDNNarchitecture,preprocessingtheaudiodata,anddesigninganeffectivetrainingscheme.WediscusspopularDNNarchitecturessuchasConvolutionalNeuralNetworks(CNNs)andRecurrentNeuralNetworks(RNNs)andtheirsuitabilityforthespecifictaskofelectronicmusicidentification.
4.DataCollectionandPreprocessing
Ahigh-qualityanddiversedatasetiscrucialforthetrainingandevaluationofDNNmodels.Wedescr