Towards explainable and interpretable musical difficulty estimation: a parameter-efficient approach

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• dc.contributor.author Ramoneda, Pedro
• dc.contributor.author Eremenko, Vsevolod
• dc.contributor.author D’Hooge, Alexandre
• dc.contributor.author Parada-Cabaleiro, Emilia
• dc.contributor.author Serra, Xavier
• dc.date.accessioned 2025-05-28T06:07:07Z
• dc.date.available 2025-05-28T06:07:07Z
• dc.date.issued 2024
• dc.description.abstract Estimating music piece difficulty is important for organizing educational music collections. This process could be partially automatized to facilitate the educator’s role. Nevertheless, the decisions performed by prevalent deeplearning models are hardly understandable, which may impair the acceptance of such a technology in music education curricula. Our work employs explainable descriptors for difficulty estimation in symbolic music representations. Furthermore, through a novel parameter-efficient white-box model, we outperform previous efforts while delivering interpretable results. These comprehensible outcomes emulate the functionality of a rubric, a tool widely used in music education. Our approach, evaluated in piano repertoire categorized in 9 classes, achieved 41.4% accuracy independently, with a mean squared error (MSE) of 1.7, showing precise difficulty estimation. Through our baseline, we illustrate how building on top of past research can offer alternatives for music difficulty assessment which are explainable and interpretable. With this, we aim to promote a more effective communication between the Music Information Retrieval (MIR) community and the music education one.
• dc.format.mimetype application/pdf
• dc.identifier.citation Ramoneda P, Eremenko VE, D'Hooge A, Parada-Cabaleiro E, Serra X. Towards explainable and interpretable musical difficulty estimation: a parameter-efficient approach. In: Kaneshiro B, Mysore G, Nieto O, Donahue C, Huang CZA, Lee JH, McFee B, McCallum M, editors. Proceedings of the 25th International Society for Music Information Retrieval Conference (ISMIR2024); 2024 November 10-14; San Francisco, USA. p. 520-8. DOI: https://www.doi.org/10.5281/zenodo.14877388
• dc.identifier.uri http://hdl.handle.net/10230/70542
• dc.language.iso eng
• dc.publisher International Society for Music Information Retrieval (ISMIR)
• dc.rights © P. Ramoneda, V. Eremenko, A. D’Hooge, E. Parada-Cabaleiro, X. Serra. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: P. Ramoneda, V. Eremenko, A. D’Hooge, E. Parada-Cabaleiro, X. Serra, “Towards Explainable and Interpretable Musical Difficulty Estimation: A parameterefficient approach”, in Proc. of the 25th Int. Society for Music Information Retrieval Conf., San Francisco, USA, 2024.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0
• dc.subject.keyword Musical difficulty estimation
• dc.title Towards explainable and interpretable musical difficulty estimation: a parameter-efficient approach
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion