Emphasizes open science, promoting open data, open software, and open access, while also being interested in exploiting results with open innovation strategies to promote the social and economic impact of their research.
Research Experience
Since his PhD, he has been working in the field of music technology, focusing on the analysis, description, and synthesis of sound and music signals. The research he currently supervises at the MTG is focused on understanding sound and music signals by combining signal processing, machine learning, and semantic technologies. They work on data-driven methodologies, developing and using large data collections, as well as knowledge-driven approaches that require domain-specific knowledge. Within their publicly and privately funded projects, they address practical problems such as music exploration and recommendation, classification of sounds, and music performance analysis for educational purposes.
Education
Obtained a PhD in Computer Music from Stanford University in 1989.
Background
Research interests: Audio Signal Processing, Sound and Music Computing, Music Information Retrieval, Computational Musicology. Full Professor of the Dept. of Engineering, Director of the Music Technology Group, Director of the UPF-BMAT Chair on AI and Music, Coordinator of the Master in Sound and Music Computing, President of Phonos Foundation.