title: GETMusic: Generating Any Music Tracks with a Unified Representation and
    Diffusion Framework
summary: Symbolic music generation aims to create musical notes, which can help users
  compose music, such as generating target instrumental tracks from scratch, or
  based on user-provided source tracks. Considering the diverse and flexible
  combination between source and target tracks, a unified model capable of
  generating any arbitrary tracks is of crucial necessity. Previous works fail to
  address this need due to inherent constraints in music representations and
  model architectures. To address this need, we propose a unified representation
  and diffusion framework named GETMusic (`GET' stands for GEnerate music
  Tracks), which includes a novel music representation named GETScore, and a
  diffusion model named GETDiff. GETScore represents notes as tokens and
  organizes them in a 2D structure, with tracks stacked vertically and
  progressing horizontally over time. During training, tracks are randomly
  selected as either the target or source. In the forward process, target tracks
  are corrupted by masking their tokens, while source tracks remain as ground
  truth. In the denoising process, GETDiff learns to predict the masked target
  tokens, conditioning on the source tracks. With separate tracks in GETScore and
  the non-autoregressive behavior of the model, GETMusic can explicitly control
  the generation of any target tracks from scratch or conditioning on source
  tracks. We conduct experiments on music generation involving six instrumental
  tracks, resulting in a total of 665 combinations. GETMusic provides
  high-quality results across diverse combinations and surpasses prior works
  proposed for some specific combinations.
paper_authors._id: 6466d83714e059dde8bbb2e9
paper_authors.name: Ang Lv
paper_authors._id: 6466d83714e059dde8bbb2ea
paper_authors.name: Xu Tan
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-06-02T03:26:15.857Z
paper_authors._id: 5f1040b6e9d71719e3be71d2
paper_authors.avatarUrl: /avatars/a2f28940236ae625ed3810ad62e343ff.svg
paper_authors.fullname: Xu Tan
paper_authors.type: user
paper_authors.user: xutan
paper_authors._id: 6466d83714e059dde8bbb2eb
paper_authors.name: Peiling Lu
paper_authors._id: 6466d83714e059dde8bbb2ec
paper_authors.name: Wei Ye
paper_authors._id: 6466d83714e059dde8bbb2ed
paper_authors.name: Shikun Zhang
paper_authors._id: 6466d83714e059dde8bbb2ee
paper_authors.name: Jiang Bian
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-26T09:09:32.000Z
paper_authors._id: 63f253f8f4e30ffd2bd308fb
paper_authors.avatarUrl: /avatars/303f4c7ee588f638acf78a7966786e1e.svg
paper_authors.fullname: Jiang Bian
paper_authors.type: user
paper_authors.user: bianjiang
paper_authors._id: 6466d83714e059dde8bbb2ef
paper_authors.name: Rui Yan
paper_title: GETMusic: Generating Any Music Tracks with a Unified Representation and
    Diffusion Framework
paper_summary: Symbolic music generation aims to create musical notes, which can help users
  compose music, such as generating target instrumental tracks from scratch, or
  based on user-provided source tracks. Considering the diverse and flexible
  combination between source and target tracks, a unified model capable of
  generating any arbitrary tracks is of crucial necessity. Previous works fail to
  address this need due to inherent constraints in music representations and
  model architectures. To address this need, we propose a unified representation
  and diffusion framework named GETMusic (`GET' stands for GEnerate music
  Tracks), which includes a novel music representation named GETScore, and a
  diffusion model named GETDiff. GETScore represents notes as tokens and
  organizes them in a 2D structure, with tracks stacked vertically and
  progressing horizontally over time. During training, tracks are randomly
  selected as either the target or source. In the forward process, target tracks
  are corrupted by masking their tokens, while source tracks remain as ground
  truth. In the denoising process, GETDiff learns to predict the masked target
  tokens, conditioning on the source tracks. With separate tracks in GETScore and
  the non-autoregressive behavior of the model, GETMusic can explicitly control
  the generation of any target tracks from scratch or conditioning on source
  tracks. We conduct experiments on music generation involving six instrumental
  tracks, resulting in a total of 665 combinations. GETMusic provides
  high-quality results across diverse combinations and surpasses prior works
  proposed for some specific combinations.
paper_ai_summary: GETMusic, a unified diffusion framework with a novel music representation, generates high-quality music tracks from scratch or conditioned on sources, outperforming previous methods in diverse combinations.
paper_ai_keywords: diffusion framework music representation GETScore GETDiff tokens 2D structure target tracks source tracks masking denoising process