# PicoAudio2: Temporal Controllable Text-to-Audio Generation with Natural Language Description

arXiv: https://arxiv.org/abs/2509.00683 · HTML: https://mlsanity.com/paper/2509.00683/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2025.

## Built on by (most cited first, 3 of 3)

- [ControlAudio: Tackling Text-Guided, Timing-Indicated and Intelligible Audio Generation via Progressive Diffusion Modeling](https://mlsanity.com/paper/2510.08878/) (2025)
- [Audio ControlNet for Fine-Grained Audio Generation and Editing](https://mlsanity.com/paper/2602.04680/) (2026)
- [Investigating Group Relative Policy Optimization for Diffusion Transformer based Text-to-Audio Generation](https://mlsanity.com/paper/2603.01565/) (2026)

## Abstract (arXiv)

While recent work in controllable text-to-audio (TTA) generation has achieved fine-grained control through timestamp conditioning, its scope remains limited by audio quality and input format. These models often suffer from poor audio quality in real datasets due to sole reliance on synthetic data. Moreover, some models are constrained to a closed vocabulary of sound events, preventing them from controlling audio generation for open-ended, free-text queries. This paper introduces PicoAudio2, a framework that advances temporal-controllable TTA by mitigating these data and architectural limitations. Specifically, we use a grounding model to annotate event timestamps of real audio-text datasets to curate temporally-strong real data, in addition to simulation data from existing works. The model is trained on the combination of real and simulation data. Moreover, we propose an enhanced architecture that integrates the fine-grained information from a timestamp matrix with coarse-grained free-text input. Experiments show that PicoAudio2 exhibits superior performance in terms of temporal controllability and audio quality.