مؤتمر
Expressive Piano Performance Rendering from Unpaired Data
| العنوان: | Expressive Piano Performance Rendering from Unpaired Data |
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| المؤلفون: | Renault, Lenny, Mignot, Rémi, Roebel, Axel |
| المساهمون: | Sciences et Technologies de la Musique et du Son (STMS), Institut de Recherche et Coordination Acoustique/Musique (IRCAM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Analyse et synthèse sonores Paris, Institut de Recherche et Coordination Acoustique/Musique (IRCAM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche et Coordination Acoustique/Musique (IRCAM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), European Project: H2020-951911,H2020-EU.2.1.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT),AI4Media(2020) |
| المصدر: | Proceedings of the 26th International Conference on Digital Audio Efects (DAFx23) ; International Conference on Digital Audio Effects (DAFx23) ; https://hal.science/hal-04221612Test ; International Conference on Digital Audio Effects (DAFx23), Sep 2023, Copenhague, Denmark. pp.355--358, ⟨10.5281/zenodo.8386761⟩ ; https://dafx23.create.aau.dkTest/ |
| بيانات النشر: | HAL CCSD |
| سنة النشر: | 2023 |
| المجموعة: | Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
| مصطلحات موضوعية: | Deep Learning, Computer Music, Performance Rendering, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD] |
| جغرافية الموضوع: | Copenhague, Denmark |
| الوصف: | International audience ; Recent advances in data-driven expressive performance rendering have enabled automatic models to reproduce the characteristics and the variability of human performances of musical compositions. However, these models need to be trained with aligned pairs of scores and performances and they rely notably on score-specific markings, which limits their scope of application. This work tackles the piano performance rendering task in a low-informed setting by only considering the score note information and without aligned data. The proposed model relies on an adversarial training where the basic score notes properties are modified in order to reproduce the expressive qualities contained in a dataset of real performances. First results for unaligned score-to-performance rendering are presented through a conducted listening test. While the interpretation quality is not on par with highly-supervised methods and human renditions, our method shows promising results for transferring realistic expressivity into scores. |
| نوع الوثيقة: | conference object |
| اللغة: | English |
| العلاقة: | info:eu-repo/grantAgreement//H2020-951911/EU/AI technology for an ethical and trustworthy European media landscape/AI4Media; hal-04221612; https://hal.science/hal-04221612Test; https://hal.science/hal-04221612/documentTest; https://hal.science/hal-04221612/file/Perf_Render_DAFx23_LBR.pdfTest |
| DOI: | 10.5281/zenodo.8386761 |
| الإتاحة: | https://doi.org/10.5281/zenodo.8386761Test https://hal.science/hal-04221612Test https://hal.science/hal-04221612/documentTest https://hal.science/hal-04221612/file/Perf_Render_DAFx23_LBR.pdfTest |
| حقوق: | http://creativecommons.org/licenses/byTest/ ; info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.5A5ED187 |
| قاعدة البيانات: | BASE |
| DOI: | 10.5281/zenodo.8386761 |
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