PerceiverS: A Multi-Scale Perceiver with Effective Segmentation for
  Long-Term Expressive Symbolic Music Generation
Authors: name: Yungang Yi, name: Weihua Li, name: Matthew Kuo, name: Quan Bai

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.