2020:Audio Classification (Train/Test) Tasks - MIREX Wiki
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Audio Latin Music Genre Classification
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1AudioClassification(Test/Train)tasks
1.1Description
1.1.1Taskspecificmailinglist
1.2Data
1.2.1AudioClassicalComposerIdentification
1.2.2AudioUSPopMusicGenreClassification
1.2.3AudioLatinMusicGenreClassification
1.2.4AudioMoodClassification
1.3AudioFormats
1.4Evaluation
1.5SubmissionFormat
1.5.1FileI/OFormat
1.5.1.1Featureextractionlistfile
1.5.1.2Traininglistfile
1.5.1.3Test(classification)listfile
1.5.1.4Classificationoutputfile
1.5.2Submissioncallingformats
1.5.2.1Examplesubmissioncallingformats
1.5.3Packagingsubmissions
1.5.4Timeandhardwarelimits
1.5.5PotentialParticipants
AudioClassification(Test/Train)tasks
Description
Manytasksinmusicclassificationcanbecharacterizedintoatwo-stageprocess:trainingclassificationmodelsusinglabeleddataandtestingthemodelsusingnew/unseendata.Therefore,weproposethis"meta"taskwhichincludesvariousaudioclassificationtasksthatfollowthisTrain/Testprocess.ForMIREX2019,sixclassificationsub-tasksareincluded:
AudioClassicalComposerIdentification
AudioUSPopMusicGenreClassification
AudioLatinMusicGenreClassification
AudioMoodClassification
AudioK-POPMoodClassification
AudioK-POPGenreClassification
AllsixclassificationtaskswereconductedinpreviousMIREXruns(pleaseseeMIREX2019Train-TestTaskResults).Thispagepresentstheevaluationresultsofthesetaskslastyear.
Taskspecificmailinglist
Inthepastwehaveuseaspecificmailinglistforthediscussionofthistaskandrelatedtasks.Thisyear,however,weareaskingthatalldiscussionstakeplaceontheMIREX"EvalFest"list.Ifyouhaveanquestionorcomment,simplyincludethetasknameinthesubjectheading.
Data
AudioClassicalComposerIdentification
Thisdatasetrequiresalgorithmstoclassifymusicaudioaccordingtothecomposerofthetrack(drawnfromacollectionofperformancesofavarietyofclassicalmusicgenres).ThecollectionusedatMIREX2009willbere-used.
Collectionstatistics:
277230-second22.05kHzmonowavclips
11"classical"composers(252clipspercomposer),including:
Bach
Beethoven
Brahms
Chopin
Dvorak
Handel
Haydn
Mendelssohnn
Mozart
Schubert
Vivaldi
AudioUSPopMusicGenreClassification
Thisdatasetrequiresalgorithmstoclassifymusicaudioaccordingtothegenreofthetrack(drawnfromacollectionofUSPopmusictracks).TheMIREX2007Genredatasetwillbere-used,whichwasdrawnfromtheUSPOP2002andUSCRAPcollections.
Collectionstatistics:
700030-secondaudioclipsin22.05kHzmonoWAVformat
10genres(700clipsfromeachgenre),including:
Blues
Jazz
Country/Western
Baroque
Classical
Romantic
Electronica
Hip-Hop
Rock
HardRock/Metal
AudioLatinMusicGenreClassification
Thisdatasetrequiresalgorithmstoclassifymusicaudioaccordingtothegenreofthetrack(drawnfromacollectionofLatinpopularanddancemusic,sourcedfromBrazilandhandlabeledbymusicexperts).CarlosSilla's(cns2(at)kent(dot)ac(dot)uk)Latinpopularanddancemusicdataset[1]willbere-used.ThiscollectionislikelytocontainagreaternumberofstylesofmusicthatwillbedifferentiatedbyrhythmiccharacteristicsthantheMIREX2007dataset.
Collectionstatistics:
3,227audiofilesin22.05kHzmonoWAVformat
10Latinmusicgenres,including:
Axe
Bachata
Bolero
Forro
Gaucha
Merengue
Pagode
Sertaneja
Tango
AudioMoodClassification
Thisdatasetrequiresalgorithmstoclassifymusicaudioaccordingtothemoodofthetrack(drawnfromacollectionofproductionmsuicsourcedfromtheAPMcollection[2]).TheMIREX2007MoodClassificationdataset[3]willbere-used.
Collectionstatistics:
60030secondaudioclipsin22.05kHzmonoWAVformatselectedfromtheAPMcollection[4],andlabeledbyhumanjudgesusingtheEvalutron6000system.
5moodcategories[5]eachofwhichcontains120clips:
Cluster_1:passionate,rousing,confident,boisterous,rowdy
Cluster_2:rollicking,cheerful,fun,sweet,amiable/goodnatured
Cluster_3:literate,poignant,wistful,bittersweet,autumnal,brooding
Cluster_4:humorous,silly,campy,quirky,whimsical,witty,wry
Cluster_5:aggressive,fiery,tense/anxious,intense,volatile,visceral
AudioFormats
Foralldatasets,participatingalgorithmswillhavetoreadaudiointhefollowingformat:
Samplerate:22KHz
Samplesize:16bit
Numberofchannels:1(mono)
Encoding:WAV
Evaluation
Thissectionfirstdescribesevaluationmethodscommontoallthedatasets,thenspecifiessettingsuniquetoeachofthetasks.
Participatingalgorithmswillbeevaluatedwith3-foldcrossvalidation.ForArtistIdentificationandClassicalComposerClassification,albumfilteringwillbeusedthetestandtrainingsplits,i.e.trainingandtestsetswillcontaintracksfromdifferentalbums;forUSPopGenreClassificationandLatinGenreClassification,artistfilteringwillbeusedthetestandtrainingsplits,i.e.trainingandtestsetswillcontaindifferentartists.
Therawclassification(identification)accuracy,standarddeviationandaconfusionmatrixforeachalgorithmwillbecomputed.
ClassificationaccuracieswillbetestedforstatisticallysignificantdifferencesusingFriedman'sAnovawithTukey-Kramerhonestlysignificantdifference(HSD)testsformultiplecomparisons.Thistestwillbeusedtorankthealgorithmsandtogroupthemintosetsofequivalentperformance.
Inadditioncomputationtimesforfeatureextractionandtraining/classificationwillbemeasured.
SubmissionFormat
FileI/OFormat
TheaudiofilestobeusedinthesetaskswillbespecifiedinasimpleASCIIlistfile.Theformatsforthelistfilesarespecifiedbelow:
Featureextractionlistfile
ThelistfilepassedforfeatureextractionwillbeasimpleASCIIlistfile.Thisfilewillcontainonepathperlinewithnoheaderline.
I.e.
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