# Dasheng AudioGen: A Unified Model for Generating Coherent Audio Scenes from Text

arXiv: https://arxiv.org/abs/2605.27838 · HTML: https://mlsanity.com/paper/2605.27838/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2026.

## Built on by (most cited first, 5 of 5)

- [AudioX-Turbo: A Unified Framework for Efficient Anything-to-Audio Generation](https://mlsanity.com/paper/2606.12555/) (2026)
- [AudioScape-TTA: A Structured Soundscape Benchmark for Fine-Grained Text-to-Audio Evaluation](https://mlsanity.com/paper/2608.04479/) (2026)
- [SonicWeave: Chunk-Routed Mixture-of-Experts for Unified Audio Scene Generation](https://mlsanity.com/paper/2608.09571/) (2026)
- [MiDashengLM-Gen: Unified Audio Scene Generation via LLM-Driven Autoregressive Flow Matching](https://mlsanity.com/paper/2608.11804/) (2026)
- [VoxAudio: Vocalized Audio Synthesis via Multi-Reward Autoregressive Flow Matching](https://mlsanity.com/paper/2608.12951/) (2026)

## Abstract (arXiv)

Audio generation has long been fragmented, with speech, music, and sound effects produced by domain-specific models that fail to jointly generate coherent audio scenes from a single description. The key obstacles are insufficient fine-grained supervision for real-world mixed audio and limited acoustic representations for modeling concurrent audio components. We present Dasheng AudioGen, a unified framework for generating general mixed-audio scenes from text. Dasheng AudioGen introduces structured multi-view captions, which explicitly decouple complex acoustic scenes into complementary description views, thereby enabling fine-grained control over audio layers. Furthermore, we employ a high-dimensional unified semantic-acoustic representation as the shared latent space. It injects semantic priors that facilitate cross-modal training convergence, while its high-dimensional feature space provides sufficient capacity to disentangle and fuse concurrent audio components effectively. With these designs, a simple flow-matching DiT achieves high-quality end-to-end audio scene generation. We also establish a comprehensive evaluation pipeline for audio scene generation. Experiments demonstrate that Dasheng AudioGen achieves performance approaching real-world recordings in mixed-audio categories, while remaining competitive with specialized models in single-type generation tasks. Demos are available at https://nieeim.github.io/Dasheng-AudioGen-Web/.