4 citations · 4 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
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…
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…
Predicting performance difficulty from piano sheet music images
Pedro Ramoneda, Jose J. Valero-Mas, Dasaem Jeong +1
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 Informat…