SYNTHESISOFTURKISHMAKAMMUSICSCORESUSINGANADAPTIVETUNINGAPPROACH
HasanSercan AtlıMusicTechnologyGroup,[email protected]
Sertan ŞentürkMusicTechnologyGroup,UPF
Barış BozkurtUniversity ofCrete
XavierSerraMusicTechnologyGroup,UPF
The7thInternationalWorkshoponFolkMusicAnalysis14~16June,Malaga
• Scoresynthesis isoneofanimportantfeature– Providesreal-timeauralfeedbackonhowthenotatedmusic
wouldsoundlike.
• Mostofthesynthesistoolsrendertheaudiodevoidoftheperformanceaddedexpression.– Scoresofmanymusicculturesdonotexplicitlyinclude
importantinformationrelatedtoperformanceaspects.• Timing,dynamics,tuning,temperamentandetc.
Introduction
• NotationeditorsarecurrentlydesignedforEurogenetic music– 12tone-equal-tempered(TET)tuningsystem– Limitedsupportforintermediatetonesandmicrotonalintervals
• Maynegativelyimpactthemusiccreationprocess– Itmightevenleadtolossofsomevariationsintheexpression
andunderstandingofthemusiccultureinthelongterm
Introduction
• AdaptiveSynthesis- Allowstheusertosynthesizethemelodyinamusicscore– accordingtoagiventuningsystem– accordingtothetuningextractedfromaudiorecordings
• Tuningandtemperamentdimensionsinmusicscoresynthesis,specificallyforTurkishmakam music– Consistsofdiversetuningsandmicrotonalintervals,whichvary
withrespecttothemakam (melodicstructure),geographicalregionandartists
Introduction
compmusic.upf.edu/node/339
1. Introduction2. TurkishMakam Music
– Makam andKarar (tonic)– MainstreamTheory,Arel-Ezgi-Uzdilek (AEU)– SymbTr ScoreCollection
3. Methodology4. Applications5. Conclusion
Outline
2.TurkishMakamMusicMakam andKarar (tonic)
8
• Melodicdimensionexplainedbymakams– Melodiesrevolvearoundasomemelodiccenters– Finaltone≈Tonic
• Nodefinitetuningreference(e.g.A4=440Hz)• Diversetuning&intonation• Allowsahighdegreeofexpressivity
2.TurkishMakamMusicMainstreamtheory,Arel-Ezgi-Uzdilek (AEU)
9
• IMPORTANT: Theoriesdoesnotnecessarilycorrespondtothepractice
• Arel-Ezgi-Uzdilek isthemainstreammusicaltheory– 24notesinanoctave– Awholetoneisdividedinto9Holderian commas(Hc)– Approximationof53tone-equal-tempered(TET)system
• 1Hc ≈22.6cents.
2.TurkishMakamMusicSymbTr ScoreCollectionv2.4.3
10
M.KemalKaraosmanoğlu.ATurkishmakammusicsymbolicdatabaseformusicinformationretrieval:SymbTr.InProceedingsof13thInternationalSocietyforMusicInformationRetrievalConference(ISMIR),pages223–228,2012.
LyricsNoteSymbols
Duration
Gün doğ ma dan a ca nım görü şelim giz li ce SAZ . . .
• Thelargestandmostrepresentativemachine-readablescorecollectionofTurkishmakam music(2200musicscores)
https://github.com/MTG/SymbTr/
• Availableindifferentformats
1. Introduction2. TurkishMakamMusic
3. Methodology– Predominantmelodyextraction– Tonicidentification– Tuninganalysisandadaptation– Scoresynthesis
5. Applications6. Conclusion
Outline
3.Methodology
12
AudioRecording PredominantMelodyExtraction
PitchDistributionTonicFrequencyIdentification
MachineReadableScore
Tuningadaptation&Synthesis
3.MethodologyPredominantMelodyExtraction
14
J.Salamon andE.Gómez,"MelodyExtractionfromPolyphonicMusicSignalsusingPitchContourCharacteristics",IEEETransactionsonAudio,SpeechandLanguageProcessing,20(6):1759-1770,Aug.2012.
3.MethodologyPredominantMelodyExtraction
15
Atlı,H.S.,Uyar,B.,Şentürk,S.,Bozkurt,B.,andSerra,X.(2014).AudiofeatureextractionforexploringTurkishmakammusic.InProceedings of3rdInternationalConferenceon AudioTechnologiesforMusicandMedia,pages142–153,Ankara,Turkey.
https://github.com/sertansenturk/predominantmelodymakam
3.MethodologyPitchDistributionComputation
17
Frekans (Hz)
Görü
lme Sık
lığı Tepe noktası
Chordia, P. & Şentürk, S. (2013). Joint recognition of raag and tonic in North Indian music. Computer Music Journal, 37(3).
Bozkurt, B. (2008). An automatic pitch analysis method for Turkish maqam music. Journal of New Music Research, 37(1), 1–13.
https://github.com/altugkarakurt/morty
3.Methodology
18
AudioRecording PredominantMelodyExtraction
PitchDistributionTonicFrequencyIdentification
3.MethodologyTonicIdentification
19
Atlı,H.S.,Bozkurt B.,&Şentürk S.(2015).AmethodfortonicfrequencyidentificationofTurkishmakammusicrecordings.5thInternationalWorkshoponFolkMusicAnalysis(FMA).119-122.
https://github.com/hsercanatli/tonicidentifier_makam
3.Methodology
20
AudioRecording PredominantMelodyExtraction
PitchDistributionTonicFrequencyIdentification
MachineReadableScore
3. MethodologyTuning analysis and adaptation
21
PitchDistribution TonicFrequencyIdentification
Frequency
Occurrence
https://github.com/miracatici/notemodel
Makam information,notes&scalecomesfrommusicscore
TuningAdapter
3. MethodologySynthesis
22
Frequency
Occurrence
https://github.com/hsercanatli/symbtrsynthesis
Synthesizedscore
MachineReadableScore
AdaptedTuning
Synthesizer
1. Introduction2. Motivation3. TurkishMakamMusic4. Methodology5. Applications
– Dunya &Dunya-web– Dunya-desktop– Application:Dunya-desktopAdaptiveSynthesisExtension
6. Conclusion
Outline
5. ApplicationsDunya-desktop Adaptive Synthesis Extension
26
https://github.com/MTG/dunya-desktop/tree/adaptive-synthesis
5. ApplicationsDunya-desktop Adaptive Synthesis Extension - Dataset
27
• 5Makams (Hicaz,Nihavent,UşşakRastandHüzzam)– Covers25%ofSymbTr Scorecollection
• 10“good-quality”recordingsforeachmakam– 50tuningpresets
• Includes;– Metadata– Predominantmelody– Pitchdistribution– Notemodels
https://github.com/MTG/otmm_tuning_intonation_dataset
• Presentedamethodologyforscoresynthesis• Developedadesktopapplication
• Futureworks– Conductuserstudies– Improvethesynthesismethodology withscore-informedtuning&intonation
analysis
Conclusion
Şentürk,S.(2016).ComputationalAnalysisofAudioRecordingsandMusicScoresfortheDescriptionandDiscoveryofOttoman-TurkishMakamMusic.PhDthesis,UniversitatPompeuFabra,Barcelona.
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