Predicting performance difficulty from piano sheet music images

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  • dc.contributor.author Ramoneda, Pedro
  • dc.contributor.author Valero-Mas, Jose J.
  • dc.contributor.author Jeong, Dasaem
  • dc.contributor.author Serra, Xavier
  • dc.date.accessioned 2023-10-24T12:18:54Z
  • dc.date.available 2023-10-24T12:18:54Z
  • dc.date.issued 2023-10-24
  • dc.description This work has been accepted at the 24th International Society for Music Information Retrieval Conference (ISMIR 2023), at Milan, Italy. October 5-9, 2023.
  • dc.description.abstract Estimating the performance difficulty of a musical score is crucial in music education for adequately designing the learning curriculum of the students. Although the Music Information Retrieval community has recently shown interest in this task, existing approaches mainly use machinereadable scores, leaving the broader case of sheet music images unaddressed. Based on previous works involving sheet music images, we use a mid-level representation, bootleg score, describing notehead positions relative to staff lines coupled with a transformer model. This architecture is adapted to our task by introducing an encoding scheme that reduces the encoded sequence length to oneeighth of the original size. In terms of evaluation, we consider five datasets—more than 7500 scores with up to 9 difficulty levels—, two of them particularly compiled for this work. The results obtained when pretraining the scheme on the IMSLP corpus and fine-tuning it on the considered datasets prove the proposal’s validity, achieving the bestperforming model with a balanced accuracy of 40.34% and a mean square error of 1.33. Finally, we provide access to our code, data, and models for transparency and reproducibility.ca
  • dc.description.sponsorship This work is funded by the Spanish Ministerio de Ciencia, Innovación y Universidades (MCIU) and the Agencia Estatal de Investigación (AEI) within the Musical AI Project – PID2019-111403GBI00/AEI/10.13039/501100011033 and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korea Government (MSIT) (NRF-2022R1F1A1074566).
  • dc.format.mimetype application/pdf*
  • dc.identifier.uri http://hdl.handle.net/10230/58122
  • dc.language.iso engca
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-111403GB-I00
  • dc.rights © P. Ramoneda, J. J. Valero-Mas, D. Jeong and X. Serra. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).ca
  • dc.rights.accessRights info:eu-repo/semantics/openAccessca
  • dc.rights.uri https://creativecommons.org/licenses/by/4.0/ca
  • dc.title Predicting performance difficulty from piano sheet music imagesca
  • dc.type info:eu-repo/semantics/preprintca
  • dc.type.version info:eu-repo/semantics/submittedVersionca

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Informes (Departament de Tecnologies de la Informació i les Comunicacions)