Notochord: a Flexible Probabilistic Model for Real-Time MIDI Performance
<|reference_start|>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.<|reference_end|>
@article{shepardson2024notochord:,
    title={Notochord: a Flexible Probabilistic Model for Real-Time MIDI Performance},
    author={Victor Shepardson, Jack Armitage and Thor Magnusson},
    journal={arXiv preprint arXiv:2403.12000},
    year={2024},
    doi={10.5281/zenodo.7088404},
    archivePrefix={arXiv},
    eprint={2403.12000},
    primaryClass={cs.SD cs.AI eess.AS}
}