# S2Accompanist: A Semantic-Aware and Structure-Guided Diffusion Model for Music Accompaniment Generation

arXiv: https://arxiv.org/abs/2605.17414 · HTML: https://mlsanity.com/paper/2605.17414/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 20 of its 26 references that are in the map.
- First posted on arXiv 17 May 2026.

## Placed in

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

## Builds on (most cited first, 20 of 20)

- [Qwen3-Omni Technical Report](https://mlsanity.com/paper/2509.17765/) (2025)
- [Simple and Controllable Music Generation](https://mlsanity.com/paper/2306.05284/) (2023)
- [MusicLM: Generating Music From Text](https://mlsanity.com/paper/2301.11325/) (2023)
- [Stable Audio Open](https://mlsanity.com/paper/2407.14358/) (2024)
- [Fast Timing-Conditioned Latent Audio Diffusion](https://mlsanity.com/paper/2402.04825/) (2024)
- [Noise2Music: Text-conditioned Music Generation with Diffusion Models](https://mlsanity.com/paper/2302.03917/) (2023)
- [MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies](https://mlsanity.com/paper/2308.01546/) (2023)
- [YuE: Scaling Open Foundation Models for Long-Form Music Generation](https://mlsanity.com/paper/2503.08638/) (2025)
- [MuQ: Self-Supervised Music Representation Learning with Mel Residual Vector Quantization](https://mlsanity.com/paper/2501.01108/) (2025)
- [ACE-Step: A Step Towards Music Generation Foundation Model](https://mlsanity.com/paper/2506.00045/) (2025)
- [Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed](https://mlsanity.com/paper/1909.01174/) (2019)
- [DiffRhythm: Blazingly Fast and Embarrassingly Simple End-to-End Full-Length Song Generation with Latent Diffusion](https://mlsanity.com/paper/2503.01183/) (2025)
- [ACE-Step 1.5: Pushing the Boundaries of Open-Source Music Generation](https://mlsanity.com/paper/2602.00744/) (2026)
- [Melody transcription via generative pre-training](https://mlsanity.com/paper/2212.01884/) (2022)
- [SongFormer: Scaling Music Structure Analysis with Heterogeneous Supervision](https://mlsanity.com/paper/2510.02797/) (2025)
- [MusicFlow: Cascaded Flow Matching for Text Guided Music Generation](https://mlsanity.com/paper/2410.20478/) (2024)
- [DiffRhythm 2: Efficient and High Fidelity Song Generation via Block Flow Matching](https://mlsanity.com/paper/2510.22950/) (2025)
- [DiffRhythm+: Controllable and Flexible Full-Length Song Generation with Preference Optimization](https://mlsanity.com/paper/2507.12890/) (2025)
- [Semantic-VAE: Semantic-Alignment Latent Representation for Better Speech Synthesis](https://mlsanity.com/paper/2509.22167/) (2025)
- [InspireMusic: Integrating Super Resolution and Large Language Model for High-Fidelity Long-Form Music Generation](https://mlsanity.com/paper/2503.00084/) (2025)

## Abstract (arXiv)

High-fidelity text-to-music generation typically relies on massive proprietary datasets and immense computational resources. Existing models often struggle to generate coherent pure musical accompaniments and lack precise, localized semantic control due to their reliance on coarse, track-level annotations. To address these limitations under constrained data and computing resources, we propose S2Accompanist, a Semantic-Aware and Structure-Guided Diffusion Model developed for the ICME2026 ATTM Grand Challenge. Specifically, we design an automated data pipeline comprising structural segmentation, Large Audio-Language Model driven segment-level captioning, and dual-metric quality grading to overcome the absence of localized metadata in raw datasets. Furthermore, we propose a semantic-aware Variational Autoencoder fine-tuning strategy that explicitly distills foundational LeadSheet structures into the acoustic latent space, effectively improving the overall audio fidelity. Extensive experiments demonstrate that S2Accompanist achieves state-of-the-art objective performance on the ATTM Grand Challenge benchmark across both the Efficiency and Performance Tracks. With only 402M parameters, our model remains competitive compared to larger-scale unconstrained models and secured first place in the Efficiency Track.