Anticipatory Music Transformer
We introduce anticipation: a method for constructing a controllable
generative model of a temporal point process (the event process) conditioned
asynchronously on realizations of a second, correlated process (the control
process). We achieve this by interleaving sequences of events and controls,
such that controls appear following stopping times in the event sequence. This
work is motivated by problems arising in the control of symbolic music
generation. We focus on infilling control tasks, whereby the controls are a
subset of the events themselves, and conditional generation completes a
sequence of events given the fixed control events. We train anticipatory
infilling models using the large and diverse Lakh MIDI music dataset. These
models match the performance of autoregressive models for prompted music
generation, with the additional capability to perform infilling control tasks,
including accompaniment. Human evaluators report that an anticipatory model
produces accompaniments with similar musicality to even music composed by
humans over a 20-second clip.
648bf0a8ded4c3eb970c8647
John Thickstun
admin_assigned
2023-06-16T16:26:42.846Z
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John Thickstun
user
jthickstun
648bf0a8ded4c3eb970c8648
David Hall
admin_assigned
2023-06-16T16:27:35.275Z
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David Hall
user
dlwh
648bf0a8ded4c3eb970c8649
Chris Donahue
admin_assigned
2023-06-16T16:27:59.520Z
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Chris Donahue
user
chrisdonahue
648bf0a8ded4c3eb970c864a
Percy Liang
admin_assigned
2023-06-16T16:28:15.548Z
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/avatars/1fb8c80b60f21f65a0a027319101f236.svg
Percy Liang
user
percyliang
Anticipatory Music Transformer
We introduce anticipation: a method for constructing a controllable
generative model of a temporal point process (the event process) conditioned
asynchronously on realizations of a second, correlated process (the control
process). We achieve this by interleaving sequences of events and controls,
such that controls appear following stopping times in the event sequence. This
work is motivated by problems arising in the control of symbolic music
generation. We focus on infilling control tasks, whereby the controls are a
subset of the events themselves, and conditional generation completes a
sequence of events given the fixed control events. We train anticipatory
infilling models using the large and diverse Lakh MIDI music dataset. These
models match the performance of autoregressive models for prompted music
generation, with the additional capability to perform infilling control tasks,
including accompaniment. Human evaluators report that an anticipatory model
produces accompaniments with similar musicality to even music composed by
humans over a 20-second clip.
A method for anticipatory generation in music control tasks, using interleaved event and control sequences, matches the performance of autoregressive models and produces high-quality accompaniments.
temporal point process event process control process stopping times symbolic music generation infilling control tasks anticipatory infilling models Lakh MIDI music dataset autoregressive models prompted music generation