国際会議ISMIR2022報告（山本分）

2023年2月27日に行われた音楽情報科学研究会の発表資料です．

Transcript

1. I S M I R ͷ Α ͔ ͬ ͨ ͱ ͜ Ζ 3 બ 2 0 2 3 . 0 2 . 2 7 S I G M U S 1 3 6 I S M I R 2 0 2 2 ใ ࠂ ʙ ͍ ͪ ࢀ Ճ ऀ ͷ ໨ ઢ ͔ Β ʙ ɹ ɹ ɹ ஜ ೾ େ ֶ ࢁ ຊ ༤ ໵ 2022.12.08 Ұॹͷϗςϧͷϝϯόʔͨͪͱɽ
2. ࣗ ݾ ঺ հ • ࢁຊ ༤໵ (΍·΋ͱ Ώ͏΍) • ஜ೾େֶ ਓͱԻͷ৘ใֶݚڀࣨʢࣉᖒݚʣ ത࢜ޙظ՝ఔ2೥ • (2022. 08-12: ؖࠃ KAIST ๚໰ݚڀһ) • J-POPͷ”ՎএςΫχοΫ”Λ൐૗͖ͭͷՎ ͔Βࣗಈݕग़͢Δٕज़ʹ͍ͭͯݱ஍Ͱൃද • Analysis and Detection of Singing Techniques in Repertoires of J-POP Solo Singers (ࢁຊ, Nam, ࣉᖒ) • Juhan Nam (KAISTʣͱͷڞಉݚڀ C U LT U R A L D I V E R S I T Y ʹ ৐ ͬͯ ண ෺ Ͱ ൃ ද ʂ
3. I S M I R ͷ ͍ ͍ ͱ ͜ Ζ ᶃ Ի ָ ৘ ใ ॲ ཧ ͷ ࠷ ઌ ୺ Λ ஌ Ε Δ
4. ஌ ݟ ͷ ๅ ݿ ʂ ૉ੖Β͍͠ݚڀͨͪ ࠷ઌ୺Λ஌ΕΔνϡʔτϦΞϧ https://ismir2022.ismir.net/ program/tutorials/
5. ٞ ࿦ Λ ଅ ͢ ۭ ؒ ͕ ੔ ͑Β Εͯ ͍ Δ I S M I R ͷ ͍ ͍ ͱ ͜ Ζ ᶄ
6. ൃ ද ͷ Α ͏͢ ޱ಄ൃද x 4෼ ϙελʔൃද x 1࣌ؒ (ݱ஍ͷΈ) օ͕͋Δఔ౓಺༰Λ஌ͬͨঢ়ଶͰདྷͯ͘ΕΔ+ ΈͬͪΓٞ࿦Ͱ͖Δʂ
7. ԕ ִ ͷ ࢀ Ճ ऀ ͱ ͷ Π ϯ λ ϥ Ϋ γ ϣ ϯ ֶձଆͰఏڙ͍ͯ͠ΔSlackͰΈͬͪΓٞ࿦ ԕִࢀՃऀͱͷϏσΦνϟοτ΋ൃੜʂ
8. ੈ ք த ͷ ݚ ڀ ऀ ͱ ͭ ͳ ͕ Γ ͕ Ͱ ͖ Δ I S M I R ͷ ͍ ͍ ͱ ͜ Ζ ᶅ
9. I S M I R ͱ ͍ ͏ ֶ ձ ͷ ಛ ௃ • ʢൺֱతʣগਓ਺ • γϯάϧτϥοΫ • ࢀՃऀಉ࢜ͷͭͳ͕ΓΛੵۃతʹଅ͍ͯ͠Δ • ΈΜͳԻָʹؔ͢ΔݚڀΛ͍ͯ͠Δʢˡେࣄʣ
10. ݱ ஍ ͳ ΒͰ ͸ ͷ ͜ ͱ όϯέοτ΍ίʔώʔλΠϜͰͷஊসλΠϜ Իָؔ࿈ͷϓϩάϥϜ ੈքதͷMIRݚڀऀͨͪͱɼԻָΛͭ·Έʹݚڀ΍ ͦΕͧΕͷจԽɾੜ׆ʹ͍ͭͯஊসͰ͖Δ
11. ηογϣϯ֎͸ࣗ༝ߦಈͰOKʂ؍ޫ͢Δ΋Α͠ɼҙؾ౤߹ͨ͠ϝϯπͰग़͔͚Δͷ΋Α͠ SlackͰ͸͓͢͢Ίεϙοτͷ৘ใ΋ϙετ͞ΕΔ
12. ݸ ਓ త ʹ ࢥ ͏ ࠃ ࡍ ձ ٞ ʢ ର ໘ ʣ ͷ ͏ · Έ • ࠷ઌ୺ͷݚڀΛ௚ʹ৮Εͯ஌Δ͜ͱ͕Ͱ͖Δ • ΈͬͪΓٞ࿦Ͱ͖Δ • ੈքதͷݚڀऀͱͷग़ձ͍͕͋Δ • ҟͳΔ஍ͰҟͳΔจԽʹ৮ΕΔ͜ͱ͕Ͱ͖Δ • ISMIR͸ͦͷ͏·ΈΛ࠷େԽͨ͠ࠃࡍձٞʂ
13. S H A L L W E I S M I R ? END
14. Ҏ߱ิ଍εϥΠυʢNot ຊฤʣ ΞϝχςΟ
15. ͓ · ͚ ɿ ࢀ Ճ ࣌ ʹ΍ ͬͯΑ ͔ ͬ ͨ ͜ ͱ 5 બ • શͯͷ࿦จʹͬ͟ͱ໨Λ௨͢ • ࣭໰Λͱʹ͔͘౤͔͚͛ΔʢSlackͰ΋ϙελʔͰ΋ʣ • ੈքͷ஍ཧ΍จԽʹৄ͘͠ͳ͓͖ͬͯɼ࿩ͷωλΛ࡞Δ • ண෺ΛணΔࣗ෼ͷࠃͰ࿩ͤΔωλΛ͓࣋ͬͯ͘ • λϒϨοτ୺຤Λങ͏ʢϙελʔൃදͰ͸ύιίϯΑΓ ॏๅʣ
16. ӳ ޠ ؔ ࿈ Ͱ ݸ ਓ త ʹ΍ ͬͯΑ ͔ ͬ ͨ ͜ ͱ • ӳޠ͕ۤख͔ͩΒͱҤॖ͗͢͠ͳ͍ • ͱʹ͔͘ϦεχϯάΛຏ͘ • पΓ͸౰ͨΓલ͕ͩωΠςΟϒεϐʔυ • ൃԻʹ͍ͭͯ஌Δͷʹ͓͢͢Ί-> https://youtu.be/nEpewJsEgUg • ֤ࠃͷᨅΓͷบ΋஌͓ͬͯ͘ͱ͍͍͔΋ • ࣌ʹ͸σδλϧΛۦ࢖ • ୯ޠ͕ࢥ͍ු͔͹ͳ͍࣌“Ah~ What should I say… (खݩͰg◦ogle຋༁)” Ͱ৐Γ੾ͬͨ৔໘͕͋ͬͨ
17. ΄ ͔ ҹ ৅ త ͩ ͬ ͨ ͜ ͱ • ؖࠃ੎ͷ୆಄ • ࠓճ20ਓ͕ۙ͘ࢀՃɽKorean-SMIR dinner͕2ճ։࠵ ʢࢁຊ͸KAISTͷΑ͠ΈͰ2ճ໨ʹ͓अຐͨ͠ʣ • एखͷΞΫςΟϒ͞ • ֶੜͰ΋ੵۃతʹslack౳ʹϙετ • ศ৐ͨ͠ˠ
18. ͓ · ͚ ɿ Π ϯ υ ͷ ͜ ΅ Ε ࿩ ᶃ • ΧϨʔͷछྨ͕๛෋ • ຖճ10छྨ͸ඞͣ͋ͬͨɽϨύʔτϦʔ΋๛෋ɽΧϨʔ޷͖ͳࢲʹ͸ఱࠃͩͬͨɽ • ຖճUberͷར༻͕େม… • ͳ͔ͳ͔͔ͭ·Βͳ͍ • ੕4ϗςϧΛࣗྗͰݟ͚͕ͭͨɼγϟτϧόε͕ఏڙ͞Ε͍ͯΔެࣜఏڙͷϗςϧʹധ ·ͬͨ΄͏͕Α͔͔ͬͨ΋ • ෲ௧ʹ஫ҙʂ • ಉߦϝϯόʔ6ਓத5ਓෲ௧ʹ…
19. ͓ · ͚ ɿ Π ϯ υ ͷ ͜ ΅ Ε ࿩ ᶄ • Χʔυ͕࢖͑ͳ͍৔ॴଟ͠ʂ • ؖࠃ͔Βͷ౉ߤͰɼݱۚ͸KRW͔࣋ͬͯ͠ͳ͔ͬͨʢͦͷ্KRW྆ସෆՄʣɽɹɹɹɹɹɹɹ ۭߓͰ൧΋৯͑ͣ1్࣌ؒํʹ฻Ε͍ͯͨ…ʢޙͰATMΛݟ͚ͭͳΜͱ͔ͳΔʣ • ࢸΔॴʹ໺ݘ͕ʂ • ೔ຊͷോ͘Β͍਺͕͍Δײ֮. ҙ֎ʹ΋ਓջ͍ͬ͜ • ձ৔ۙ͘Ͱ͸໺ੜͷݘͱࣛʁ͕݌՞͢Δϋϓχϯά΋ • ंͷΫϥΫγϣϯͷԻ৭ͷଟ༷͞ • ं͸΢ΠϯΧʔͷ୅ΘΓʹΫϥΫγϣϯΛ࢖͏จԽɽָثΈ͍ͨͰ֗த͕·ΔͰύϨʔυ
20. ҹ ৅ ʹ ࢒ ͬ ͨ ݚ ڀ ᶃ ɿ ଟ ໘ త ͳ ࣝ ผ • Իָͷ΋ͭෳ਺ͷཁૉʹண໨͠ɼෳ਺ͷಛ௃Λ࢖͏ • End-to-End Lyrics Transcription Informed by Pitch and Onset Estimation ՎࢺࣝผʹϐονͱΦϯηοτΛ࢖͏ • And what if two musical versions don't share melody, harmony, rhythm, or lyrics? ΧόʔιϯάࣝผʹϐονɼϋʔϞχʔɼϦζ ϜɼՎࢺಛ௃Λ૊Έ߹ΘͤɼSoTA ʢଟ༷ͳόʔδϣϯҧ͍ʹదԠʣ • Verse versus Chorus: Structure-aware Feature Extraction for Lyrics-based Genre Recognition ՎࢺϕʔεͷδϟϯϧࣝผʹʮͲ ͷηΫγϣϯͷՎࢺ͔ʁʯͷӨڹΛௐࠪ
21. • సҠֶश • Melody transcription via generative pre-training ϝϩσΟͷָේΛग़ྗɼJukebox΍MT3ͷembeddingΛར༻ • Singing beat tracking with Self-supervised front-end and linear transformers Վ”ͷΈ”Λೖྗͱ͢ΔϏʔτਪఆɽWavLM΍ DistillHuBERTΛೖྗʹར༻ • Transfer Learning of wav2vec 2.0 for Automatic Lyric Transcription Wav2Vec2.0Λ༻͍ͨՎࢺࣝผɼগͳֶ͍शσʔλͰ΋େྔσʔ λͰֶशͤͨ͞Ϟσϧʹඖఢ͢ΔੑೳΛ࣮ݱ • σʔλ֦ு • PDAugment: Data Augmentation by Pitch and Duration Adjustments for Automatic Lyrics Transcription େྔͷ࿩੠Λ”ϝϩσΟ Խ”ֶ͠शσʔλʹՃ͑ΔՎࢺࣝผ • Scaling Polyphonic Transcription with Mixtures of Monophonic Transcriptions ෳ਺ͷ୯Իͷԋ૗σʔλΛॏͶ߹ΘͤͨσʔλΛֶ शͨ͠ෳ਺ָث࠾ේ • ίετߟྀܕֶश • Toward postprocessing-free neural networks for joint beat and downbeat estimation ޮ཰తͳ৞ΈࠐΈϞδϡʔϧʢSeparable convʣͱଛࣦؔ਺ʢFocal loss+DICE lossʣΛར༻ͨ͠ޙॲཧ͍ΒͣͷϏʔτਪఆ • Analysis and Detection of Singing Techniques in Repertoires of J-POP Solo Singers (ࣗ෼ͷ) Focal lossΛར༻͠୹͍ՎএςΫχο Ϋͷݕग़ੑೳUp ҹ ৅ ʹ ࢒ ͬ ͨ ݚ ڀ ᶄ ɿ σ ʔ λ ෆ ଍ ΁ ͷ ର Ԡ
22. • Traces of Globalization in Online Music Consumption Patterns and Results of Recommendation Algorithms ԻָਪનγεςϜ ͕ԻָͷGlobalizationΛଅ͔͢Ͳ͏͔Λௐ΂ͨ -> USԻָ͕Ϛʔ έοτΛಠ઎͍ͯ͠Δࠃ͕ଟ͘ɼNNϕʔεͷਪનγεςϜ͕ GlobalizationΛଅ͕͢USԻָͷಠ઎ੑ΋ॿ௕͢Δ͜ͱΛࣔࠦ • Violin Etudes: A Comprehensive Dataset for f0 Estimation and Performance Analysis όΠΦϦϯԋ૗ͷͨΊͷf0ͷΞϊςʔγϣ ϯσʔληοτ CREPE୯ମͰ͸ਖ਼͘͠ਪఆͰ͖ͳ͍͜ͱΛ֬ೝ ͠ɼஞ࣍తʹf0Λਪఆ->ֶशʹՃ͑Δͱ͍͏ϧʔϓͰσʔληο τΛߏங ҹ ৅ ʹ ࢒ ͬ ͨ ݚ ڀ ᶅ ɿ ͦ ͷ ଞ ॾ ʑ