My research is motivated by my background in both engineering and music, and is centred around the Music Information Retrieval field. I try to understand the way humans describe music (in terms of melody, tonality, similarity, style or emotion) and emulate these descriptions with intelligent systems dealing with large datasets.
Starting from music, I research on the impact of artificial intelligence (AI) into human behaviour. My interests are on the impact of AI on how we work, our decisions, creativity and children´s cognitive and socio-emotional development.
Current/recent research projects:
- Human behaviour and Machine Intelligence– HUMAINT (Lead Scientist) Centre for Advanced Studies, Joint Research Centre, European Commission, 2018-2020.
- Towards Richer Online Music Public-domain Archives- TROMPA (PI), H2020 project coordinated by Universitat Pompeu Fabra, Barcelona, 2018-2021.
- Cante Flamenco Tech (Technical development) – Technologies for learning to sing flamenco, personal project.
- Banda sonora vital (Researcher) – Technologies for life sound recovery and music recommendation for people with dementia, personal project.
- CASAS (PI – Spanish Government): Community Assisted Singing Analysis and Synthesis 2016-2018.
- PHENICX (PI – European Commission FP7 STREP): Changing the way we experience classical music concerts, 2013-2016.
Some technologies I have contributed to:
- Essentia: Open-source library and tools for audio and music analysis, description and synthesis (with MTG group).
- MELODIA: Melody extraction vamp plugin (with Justin Salamon)
- HPCP: Harmonic Pitch Class Profiles vamp plugin (with Jordi Bonada)
- Matlab toolbox for multi-scale set-class analysis
- CANTE: Automatic transcription of flamenco singing,