PerceiverS: A Multi-Scale Perceiver with Effective Segmentation for Long-Term Expressive Symbolic Music Generation

AI-based music generation has progressed significantly in recent years. However, creating symbolic music that is both long-structured and expressive remains a considerable 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 musical diversity. The proposed model has been evaluated using the Maestro dataset and has demonstrated improvements in generating music of conventional length with expressive nuances. The project demos and the generated music samples can be accessed through the link: https://perceivers.github.io