title: Anticipatory Music Transformer
summary: 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.
paper_authors._id: 648bf0a8ded4c3eb970c8647
paper_authors.name: John Thickstun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-16T16:26:42.846Z
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paper_authors.fullname: John Thickstun
paper_authors.type: user
paper_authors.user: jthickstun
paper_authors._id: 648bf0a8ded4c3eb970c8648
paper_authors.name: David Hall
paper_authors.status: admin_assigned
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paper_authors._id: 624c7608d84cc6ac390b7a8d
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paper_authors.fullname: David Hall
paper_authors.type: user
paper_authors.user: dlwh
paper_authors._id: 648bf0a8ded4c3eb970c8649
paper_authors.name: Chris Donahue
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-16T16:27:59.520Z
paper_authors._id: 63b1b5f034b91b8272aac6c8
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paper_authors.fullname: Chris Donahue
paper_authors.type: user
paper_authors.user: chrisdonahue
paper_authors._id: 648bf0a8ded4c3eb970c864a
paper_authors.name: Percy Liang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-16T16:28:15.548Z
paper_authors._id: 6409651b9e9f790c905b2335
paper_authors.avatarUrl: /avatars/1fb8c80b60f21f65a0a027319101f236.svg
paper_authors.fullname: Percy Liang
paper_authors.type: user
paper_authors.user: percyliang
paper_title: Anticipatory Music Transformer
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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