Pedro Ramoneda

Bringing together Music Education and AI

About

Technology and music are my passions. It is my primary motivation to pursue research in sound and music computing and music information retrieval. I have previously researched on Harmony-related topics, and I am currently pursuing my PhD studies on the intersection between music performance and artificial intelligence at Music Technology Group , Universitat Pompeu Fabra, Barcelona.

My PhD thesis, "Can I play it? Understanding piano performance difficulty through explainable and multimodal machine learning" focuses on audio and symbolic music dataset creation and deep learning for performance difficulty analysis under the supervision of Prof. Xavier Serra . We aim to understand the performance difficulty, through pedagogically motivated representations, to recommend a customised and optimised learning path for each music student and allow musicians explore large music score collections. My ML research interests include explainable machine learning, generative models, human-centered AI and curriculum learning.

During the summer of 2023, I embarked on a research internship under the supervision of Taketo Akama at Sony CSL (Tokyo). My attention was captured by the fascinating field of music generation, particularly the challenge of inpainting to complete missing sections of musical compositions. This hands-on experience was crucial in deepening my understanding of music generation, and it has significantly heightened my interest in this area of study. Currently, I am actively collaborating with my colleague and mentor, Dasaem Jeong, on developing innovative music generation systems that are specifically designed to enhance music education. Together, we are working to create user-friendly and effective tools that support both educators and learners in their musical journeys.