Combining piano performance dimensions for score difficulty classification

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• dc.contributor.author Ramoneda, PedroRamoneda, Pedro
• dc.contributor.author Jeong, Dasaem
• dc.contributor.author Eremenko, Vsevolod
• dc.contributor.author Tamer, Nazif Can
• dc.contributor.author Miron, Marius
• dc.contributor.author Serra, Xavier
• dc.date.accessioned 2023-10-26T07:11:57Z
• dc.date.available 2023-10-26T07:11:57Z
• dc.date.issued 2024
• dc.description.abstract Predicting the difficulty of playing a musical score is essential for structuring and exploring score collections. Despite its importance for music education, the automatic difficulty classification of piano scores is not yet solved, mainly due to the lack of annotated data and the subjectiveness of the annotations. This paper aims to advance the state-of-the-art in score difficulty classification with two major contributions. To address the lack of data, we present Can I Play It? (CIPI) dataset, a machine-readable piano score dataset with difficulty annotations obtained from the renowned classical music publisher Henle Verlag. The dataset is created by matching public domain scores with difficulty labels from Henle Verlag, then reviewed and corrected by an expert pianist. As a second contribution, we explore various input representations from score information to pre-trained ML models for piano fingering and expressiveness inspired by the musicology definition of performance. We show that combining the outputs of multiple classifiers performs better than the classifiers on their own, pointing to the fact that the representations capture different aspects of difficulty. In addition, we conduct numerous experiments that lay a foundation for score difficulty classification and create a basis for future research. Our best-performing model reports a 39.5% balanced accuracy and 1.1 median square error across the nine difficulty levels proposed in this study. Code, dataset, and models are made available for reproducibility.
• dc.description.sponsorship This work is supported in part by the project Musical AI - PID2019- 111403GB-I00/AEI/10.13039/501100011033 funded by the Spanish Ministerio de Ciencia, Innovacion Universidades (MCIU) and the Agencia Estatal de Investigacion (AEI) and Sogang University Research Grant of 202110035.01.
• dc.format.mimetype application/pdf
• dc.identifier.citation Ramoneda P, Jeong D, Eremenko V, Tamer NC, Miron M, Serra X. Combining piano performance dimensions for score difficulty classification. Expert Syst Appl. 2024;238(Part B):121776. DOI: 10.1016/j.eswa.2023.121776
• dc.identifier.doi http://dx.doi.org/10.1016/j.eswa.2023.121776
• dc.identifier.issn 0957-4174
• dc.identifier.uri http://hdl.handle.net/10230/58137
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof Expert Systems with Applications. 2024;238(Part B):121776.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/2PE/PID2019-111
• dc.rights © 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync- nd/4.0/).
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
• dc.subject.keyword Performance difficulty prediction
• dc.subject.keyword Education technology
• dc.subject.keyword Music complexity
• dc.subject.keyword Music difficulty
• dc.subject.keyword Difficulty analysis
• dc.subject.keyword Performance difficulty
• dc.subject.keyword Can I play it
• dc.subject.keyword Music playability
• dc.subject.keyword Piano fingering
• dc.subject.keyword Expressive piano performance
• dc.subject.keyword Music information retrieval
• dc.title Combining piano performance dimensions for score difficulty classification
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion