PerceiverS: A Multi-Scale Perceiver with Effective Segmentation for
  Long-Term Expressive Symbolic Music Generation
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.
6736129a7506985f27806752
Yungang Yi
admin_assigned
2024-11-15T17:25:42.110Z
64c55d263e30498ccaa1f38f
https://cdn-avatars.huggingface.co/v1/production/uploads/64c55d263e30498ccaa1f38f/-4eWNq0m_Vye5iKc6XzFJ.png
Yungang Yi
user
perceivers
6736129a7506985f27806753
Weihua Li
admin_assigned
2024-11-15T14:23:43.336Z
669b7a27dac1eb34c020dd34
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/v8ndQr12Oy0cJSLFXu2vx.png
Weihua Li
user
liweihuamark
6736129a7506985f27806754
Matthew Kuo
admin_assigned
2024-11-15T14:23:50.690Z
64657e97e27766e8921b7b4b
/avatars/0e34f2b291e2d64b9c378e6abfc280a5.svg
Matthew Kuo
user
mkuo
6736129a7506985f27806755
Quan Bai
PerceiverS: A Multi-Scale Perceiver with Effective Segmentation for
  Long-Term Expressive Symbolic Music Generation
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.
PerceiverS, using Effective Segmentation and Multi-Scale attention, enhances long-structured and expressive symbolic music generation by combining cross-attention and self-attention.
Effective Segmentation Multi-Scale attention mechanisms cross-attention self-attention long-range musical structure performance nuances