Can audio reveal music performance difficulty? Insights from the piano syllabus dataset

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• dc.contributor.author Ramoneda, Pedro
• dc.contributor.author Lee, Minhee
• dc.contributor.author Jeong, Dasaem
• dc.contributor.author Valero-Mas, José J.
• dc.contributor.author Serra, Xavier
• dc.date.accessioned 2025-03-27T07:29:12Z
• dc.date.embargoEnd info:eu-repo/date/embargoEnd/2027-02-07
• dc.date.issued 2025
• dc.description.abstract Automatically estimating the performance difficulty of a music piece represents a key process in music education to create tailored curricula according to the individual needs of the students. Given its relevance, the Music Information Retrieval (MIR) field comprises some proof-of-concept works addressing this task that mainly focus on high-level music abstractions such as machine-readable scores or music sheet images. In this regard, the potential of directly analyzing audio recordings has generally been neglected. This work addresses this gap in the field with two contributions: (i) PSyllabus, the first audio-based difficulty estimation dataset—collected from Piano Syllabus community—featuring 7,901 piano pieces across 11 difficulty levels from 1,233 composers as well as two additional benchmark datasets particularly compiled for evaluation purposes; and (ii) a recognition framework capable of managing different input representations—both in unimodal and multimodal manners—derived from audio to perform the difficulty estimation task. The comprehensive experimentation comprising different pre-training schemes, input modalities, and multi-task scenarios proves the validity of the hypothesis and establishes PSyllabus as a reference dataset for audio-based difficulty estimation in the MIR field. The dataset, developed code, and trained models are publicly shared to promote further research in the field.
• dc.embargo.liftdate 2027-02-07
• dc.format.mimetype application/pdf
• dc.identifier.citation Ramoneda P, Lee M, Jeong D, Valero-Mas JJ, Serra X. Can audio reveal music performance difficulty? Insights from the piano syllabus dataset. IEEE Trans Audio Speech Lang Process. 2025;33:1129-41. DOI: 10.1109/TASLPRO.2025.3539018
• dc.identifier.doi http://dx.doi.org/10.1109/TASLPRO.2025.3539018
• dc.identifier.issn 2998-4173
• dc.identifier.uri http://hdl.handle.net/10230/70027
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof IEEE Transactions on Audio, Speech and Language Processing. 2025;33:1129-41
• dc.rights © 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/TASLPRO.2025.3539018
• dc.rights.accessRights info:eu-repo/semantics/embargoedAccess
• dc.subject.keyword Music difficulty
• dc.subject.keyword Music information retrieval
• dc.subject.keyword Music technology education
• dc.subject.keyword Performance analysis
• dc.subject.keyword Playability
• dc.title Can audio reveal music performance difficulty? Insights from the piano syllabus dataset
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion