4 citations · 7 across the 10 of their papers we have counts for
11 papers · 1 filter
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…
Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets
Pedro Ramoneda, Pablo Alonso-Jiménez, Sergio Oramas +2
Music autotagging aims to automatically assign descriptive tags, such as genre, mood, or instrumentation, to audio recordings. Due to its challenges, diversity of semantic descript…
OMAR-RQ: Open Music Audio Representation Model Trained with Multi-Feature Masked Token Prediction
Pablo Alonso-Jiménez, Pedro Ramoneda, R. Oguz Araz +2
Developing open-source foundation models is essential for advancing research in music audio understanding and ensuring access to powerful, multipurpose representations for music in…
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…
Music Proofreading with RefinPaint: Where and How to Modify Compositions given Context
Pedro Ramoneda, Martin Rocamora, Taketo Akama
Autoregressive generative transformers are key in music generation, producing coherent compositions but facing challenges in human-machine collaboration. We propose RefinPaint, an…
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…
Combining piano performance dimensions for score difficulty classification
Pedro Ramoneda, Dasaem Jeong, Vsevolod Eremenko +3
Predicting the difficulty of playing a musical score is essential for structuring and exploring score collections. Despite its importance for music education, the automatic difficu…
Score difficulty analysis for piano performance education based on fingering
Pedro Ramoneda, Nazif Can Tamer, Vsevolod Eremenko +2
In this paper, we introduce score difficulty classification as a sub-task of music information retrieval (MIR), which may be used in music education technologies, for personalised…