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cs.SD2025

Difficulty-Controlled Simplification of Piano Scores with Synthetic Data for Inclusive Music Education

Pedro Ramoneda, Emilia Parada-Cabaleiro, Dasaem Jeong +1

Despite its potential, AI advances in music education are hindered by proprietary systems that limit the democratization of technology in this domain. In particular, AI-driven musi…

cs.SD2025

Difficulty-Aware Score Generation for Piano Sight-Reading

Pedro Ramoneda, Masahiro Suzuki, Akira Maezawa +1

Adapting learning materials to the level of skill of a student is important in education. In the context of music training, one essential ability is sight-reading -- playing unfami…

cs.SD2025

Can Audio Reveal Music Performance Difficulty? Insights from the Piano Syllabus Dataset

Pedro Ramoneda, Minhee Lee, Dasaem Jeong +2

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 t…

cs.SD2024

The Role of Large Language Models in Musicology: Are We Ready to Trust the Machines?

Pedro Ramoneda, Emilia Parada-Cabaleiro, Benno Weck +1

In this work, we explore the use and reliability of Large Language Models (LLMs) in musicology. From a discussion with experts and students, we assess the current acceptance and co…

cs.SD2024

Towards Explainable and Interpretable Musical Difficulty Estimation: A Parameter-efficient Approach

Pedro Ramoneda, Vsevolod Eremenko, Alexandre D'Hooge +2

Estimating music piece difficulty is important for organizing educational music collections. This process could be partially automatized to facilitate the educator's role. Neverthe…