# Efficient Long-Sequence Diffusion Modeling for Symbolic Music Generation

arXiv: https://arxiv.org/abs/2603.00576 · HTML: https://mlsanity.com/paper/2603.00576/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 36 of its 48 references that are in the map.
- First posted on arXiv 28 Feb 2026.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 30 of 36)

- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Structured Denoising Diffusion Models in Discrete State-Spaces](https://mlsanity.com/paper/2107.03006/) (2021)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [Simple and Effective Masked Diffusion Language Models](https://mlsanity.com/paper/2406.07524/) (2024)
- [Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset](https://mlsanity.com/paper/1810.12247/) (2018)
- [Symbolic Music Generation with Diffusion Models](https://mlsanity.com/paper/2103.16091/) (2021)
- [MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies](https://mlsanity.com/paper/2308.01546/) (2023)
- [Long-form music generation with latent diffusion](https://mlsanity.com/paper/2404.10301/) (2024)
- [POP909: A Pop-song Dataset for Music Arrangement Generation](https://mlsanity.com/paper/2008.07142/) (2020)
- [DiM: Diffusion Mamba for Efficient High-Resolution Image Synthesis](https://mlsanity.com/paper/2405.14224/) (2024)
- [This Time with Feeling: Learning Expressive Musical Performance](https://mlsanity.com/paper/1808.03715/) (2018)
- [Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection](https://mlsanity.com/paper/2406.10700/) (2024)
- [Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion](https://mlsanity.com/paper/2402.14285/) (2024)
- [Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series](https://mlsanity.com/paper/2410.19538/) (2024)
- [Video2Music: Suitable Music Generation from Videos using an Affective Multimodal Transformer model](https://mlsanity.com/paper/2311.00968/) (2023)
- [Polyffusion: A Diffusion Model for Polyphonic Score Generation with Internal and External Controls](https://mlsanity.com/paper/2307.10304/) (2023)
- [Whole-Song Hierarchical Generation of Symbolic Music Using Cascaded Diffusion Models](https://mlsanity.com/paper/2405.09901/) (2024)
- [Text2midi: Generating Symbolic Music from Captions](https://mlsanity.com/paper/2412.16526/) (2024)
- [Discrete Diffusion Probabilistic Models for Symbolic Music Generation](https://mlsanity.com/paper/2305.09489/) (2023)
- [XMusic: Towards a Generalized and Controllable Symbolic Music Generation Framework](https://mlsanity.com/paper/2501.08809/) (2025)
- [PianoBART: Symbolic Piano Music Generation and Understanding with Large-Scale Pre-Training](https://mlsanity.com/paper/2407.03361/) (2024)
- [InspireMusic: Integrating Super Resolution and Large Language Model for High-Fidelity Long-Form Music Generation](https://mlsanity.com/paper/2503.00084/) (2025)
- [Survey on the Evaluation of Generative Models in Music](https://mlsanity.com/paper/2506.05104/) (2025)
- [Composer Style-specific Symbolic Music Generation Using Vector Quantized Discrete Diffusion Models](https://mlsanity.com/paper/2310.14044/) (2023)
- [Motifs, Phrases, and Beyond: The Modelling of Structure in Symbolic Music Generation](https://mlsanity.com/paper/2403.07995/) (2024)
- [Structure-informed Positional Encoding for Music Generation](https://mlsanity.com/paper/2402.13301/) (2024)
- [MoMu-Diffusion: On Learning Long-Term Motion-Music Synchronization and Correspondence](https://mlsanity.com/paper/2411.01805/) (2024)
- [MusicMamba: A Dual-Feature Modeling Approach for Generating Chinese Traditional Music with Modal Precision](https://mlsanity.com/paper/2409.02421/) (2024)
- [Mamba-Diffusion Model with Learnable Wavelet for Controllable Symbolic Music Generation](https://mlsanity.com/paper/2505.03314/) (2025)

## Built on by (most cited first, 1 of 1)

- [Agogic: Performance-Timed Music Tokens for LLM-Native Text-to-Symbolic-Music Generation](https://mlsanity.com/paper/2608.03999/) (2026)

## Abstract (arXiv)

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 efficiency, and it has robust generalization to underexplored musical styles. These results show that SMDIM offers a principled solution for long-sequence symbolic music generation, including associated attributes that accompany the sequences. We provide a project webpage with audio examples and supplementary materials at https://3328702107.github.io/smdim-music/.