title: PerceiverS: A Multi-Scale Perceiver with Effective Segmentation for
    Long-Term Expressive Symbolic Music Generation
summary: Music generation has progressed significantly, especially in the domain of
  audio generation. However, generating symbolic music that is both
  long-structured and expressive remains a significant challenge. In this paper,
  we propose PerceiverS (Segmentation and Scale), a novel architecture designed
  to address this issue by leveraging both Effective Segmentation and Multi-Scale
  attention mechanisms. Our approach enhances symbolic music generation by
  simultaneously learning long-term structural dependencies and short-term
  expressive details. By combining cross-attention and self-attention in a
  Multi-Scale setting, PerceiverS captures long-range musical structure while
  preserving performance nuances. The proposed model, evaluated on datasets like
  Maestro, demonstrates improvements in generating coherent and diverse music
  with both structural consistency and expressive variation. The project demos
  and the generated music samples can be accessed through the link:
  https://perceivers.github.io.
paper_authors._id: 6736129a7506985f27806752
paper_authors.name: Yungang Yi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-15T17:25:42.110Z
paper_authors._id: 64c55d263e30498ccaa1f38f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64c55d263e30498ccaa1f38f/-4eWNq0m_Vye5iKc6XzFJ.png
paper_authors.fullname: Yungang Yi
paper_authors.type: user
paper_authors.user: perceivers
paper_authors._id: 6736129a7506985f27806753
paper_authors.name: Weihua Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-15T14:23:43.336Z
paper_authors._id: 669b7a27dac1eb34c020dd34
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/v8ndQr12Oy0cJSLFXu2vx.png
paper_authors.fullname: Weihua Li
paper_authors.type: user
paper_authors.user: liweihuamark
paper_authors._id: 6736129a7506985f27806754
paper_authors.name: Matthew Kuo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-15T14:23:50.690Z
paper_authors._id: 64657e97e27766e8921b7b4b
paper_authors.avatarUrl: /avatars/0e34f2b291e2d64b9c378e6abfc280a5.svg
paper_authors.fullname: Matthew Kuo
paper_authors.type: user
paper_authors.user: mkuo
paper_authors._id: 6736129a7506985f27806755
paper_authors.name: Quan Bai
paper_title: PerceiverS: A Multi-Scale Perceiver with Effective Segmentation for
    Long-Term Expressive Symbolic Music Generation
paper_summary: Music generation has progressed significantly, especially in the domain of
  audio generation. However, generating symbolic music that is both
  long-structured and expressive remains a significant challenge. In this paper,
  we propose PerceiverS (Segmentation and Scale), a novel architecture designed
  to address this issue by leveraging both Effective Segmentation and Multi-Scale
  attention mechanisms. Our approach enhances symbolic music generation by
  simultaneously learning long-term structural dependencies and short-term
  expressive details. By combining cross-attention and self-attention in a
  Multi-Scale setting, PerceiverS captures long-range musical structure while
  preserving performance nuances. The proposed model, evaluated on datasets like
  Maestro, demonstrates improvements in generating coherent and diverse music
  with both structural consistency and expressive variation. The project demos
  and the generated music samples can be accessed through the link:
  https://perceivers.github.io.
paper_ai_keywords: PerceiverS Effective Segmentation Multi-Scale attention mechanisms long-term structural dependencies short-term expressive details cross-attention self-attention Multi-Scale setting Maestro long-range musical structure performance nuances structural consistency expressive variation