Jack Armitage
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Jack Armitage has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Victor Shepardson
Intelligent Instruments Lab Iceland University of the Arts Reykjavík Iceland
2 shared publications - Adan L Benito Temprano
Centre for Digital Music, Queen Mary University of London, London, United Kingdom
1 shared publication - Andrew McPherson
Queen Mary University of London
1 shared publication - Chris Kiefer
Experimental Music Technologies Lab, Department of Music, University of Sussex, Brighton, United Kingdom
1 shared publication - Franco Santiago Caspe
Centre for Digital Music Queen Mary University of London London UK
1 shared publication - Rebecca Fiebrink
Creative Computing Institute, University of the Arts London, London, United Kingdom
1 shared publication
Latest Publications
- Open Access
Notochord: a Flexible Probabilistic Model for Real-Time MIDI Performance
Deep learning-based probabilistic models of musical data are producing increasingly realistic results and promise to enter creative workflows of many kinds. Yet they have been little-studied in a performance setting, where the results of user actions typically ought to feel instantaneous. To enable such study, we designed Notochord, a deep probabilistic model for sequences of structured events, and trained an instance of it on the Lakh MIDI dataset. Our probabilistic formulation allows interpretable interventions at a sub-event level, which enables one model to act as a backbone for diverse interactive musical functions including steerable generation, harmonization, machine improvisation, and likelihood-based interfaces. Notochord can generate polyphonic and multi-track MIDI, and respond to inputs with latency below ten milliseconds. Training code, model checkpoints and interactive examples are provided as open source software.
Open AccessNotochord: a Flexible Probabilistic Model for Real-Time MIDI Performance
Deep learning-based probabilistic models of musical data are producing increasingly realistic results and promise to enter creative workflows of many kinds. Yet they have been little-studied in a performance setting, where the results of user actions typically ought to feel instantaneous. To enable such study, we designed Notochord, a deep probabilistic model for sequences of structured events, and trained an instance of it on the Lakh MIDI dataset. Our probabilistic formulation allows interpretable interventions at a sub-event level, which enables one model to act as a backbone for diverse interactive musical functions including steerable generation, harmonization, machine improvisation, and likelihood-based interfaces. Notochord can generate polyphonic and multi-track MIDI, and respond to inputs with latency below ten milliseconds. Training code, model checkpoints and interactive examples are provided as open source software.