[2603.00576] Efficient Long-Sequence Diffusion Modeling for Symbolic Music Generation

Computer Science > Sound arXiv:2603.00576 (cs) [Submitted on 28 Feb 2026] Title:Efficient Long-Sequence Diffusion Modeling for Symbolic Music Generation Authors:Jinhan Xu, Xing Tang, Houpeng Yang, Haoran Zhang, Shenghua Yuan, Jiatao Chen, Tianming Xi, Jing Wang, Jiaojiao Yu, Guangli Xiang View a PDF of the paper titled Efficient Long-Sequence Diffusion Modeling for Symbolic Music Generation, by Jinhan Xu and 9 other authors View PDF HTML (experimental) Abstract:Symbolic music generation is a challenging task in multimedia generation, involving long sequences with hierarchical temporal structures, long-range dependencies, and fine-grained local details. Though recent diffusion-based models produce high quality generations, they tend to suffer from high training and inference costs with long symbolic sequences due to iterative denoising and sequence-length-related costs. To deal with such problem, we put forth a diffusing strategy named SMDIM to combine efficient global structure construction and light local refinement. SMDIM uses structured state space models to capture long range musical context at near linear cost, and selectively refines local musical details via a hybrid refinement scheme. Experiments performed on a wide range of symbolic music datasets which encompass various Western classical music, popular music and traditional folk music show that the SMDIM model outperforms the other state-of-the-art approaches on both the generation quality and the computational e...