# AudioTime: A Temporally-aligned Audio-text Benchmark Dataset

arXiv: https://arxiv.org/abs/2407.02857 · HTML: https://mlsanity.com/paper/2407.02857/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2024.

## Built on by (most cited first, 19 of 19)

- [AudioX: A Unified Framework for Anything-to-Audio Generation](https://mlsanity.com/paper/2503.10522/) (2025)
- [Both Ears Wide Open: Towards Language-Driven Spatial Audio Generation](https://mlsanity.com/paper/2410.10676/) (2024)
- [FreeAudio: Training-Free Timing Planning for Controllable Long-Form Text-to-Audio Generation](https://mlsanity.com/paper/2507.08557/) (2025)
- [MMMG: a Comprehensive and Reliable Benchmark for Multitask Multimodal Generation](https://mlsanity.com/paper/2505.17613/) (2025)
- [MambaFoley: Foley Sound Generation using Selective State-Space Models](https://mlsanity.com/paper/2409.09162/) (2024)
- [RiTTA: Modeling Event Relations in Text-to-Audio Generation](https://mlsanity.com/paper/2412.15922/) (2024)
- [DegDiT: Controllable Audio Generation with Dynamic Event Graph Guided Diffusion Transformer](https://mlsanity.com/paper/2508.13786/) (2025)
- [AuditoryBench++: Can Language Models Understand Auditory Knowledge without Hearing?](https://mlsanity.com/paper/2509.17641/) (2025)
- [Towards Fine-grained Temporal Perception: Post-Training Large Audio-Language Models with Audio-Side Time Prompt](https://mlsanity.com/paper/2604.13715/) (2026)
- [AudioX-Turbo: A Unified Framework for Efficient Anything-to-Audio Generation](https://mlsanity.com/paper/2606.12555/) (2026)
- [Membership Inference for Contrastive Pre-training Models with Text-only PII Queries](https://mlsanity.com/paper/2603.14222/) (2026)
- [EvA: An Evidence-First Audio Understanding Paradigm for LALMs](https://mlsanity.com/paper/2603.27667/) (2026)
- [Auto-AEG: Scalable Data Construction for Open-Vocabulary Audio Event Grounding](https://mlsanity.com/paper/2607.04383/) (2026)
- [A Production-Oriented Framework for Evaluation of SFX Generation](https://mlsanity.com/paper/2607.09973/) (2026)
- [Improving Text-to-Audio Instruction Following via Fine-Grained Feedback from Audio-Aware Large Language Models](https://mlsanity.com/paper/2607.13408/) (2026)
- [An Evaluation Framework for Structured Audio Captions Validated by Controlled Perturbations](https://mlsanity.com/paper/2607.21424/) (2026)
- [AI-Based Sound Effect Generation: A Narrative Review of Generative Models Across Input Modalities](https://mlsanity.com/paper/2608.03742/) (2026)
- [AudioScape-TTA: A Structured Soundscape Benchmark for Fine-Grained Text-to-Audio Evaluation](https://mlsanity.com/paper/2608.04479/) (2026)
- [What Did the MLLM Hear? Token-Level Spectro-Temporal Grounding for Audio MLLM Explainability](https://mlsanity.com/paper/2609.12663/) (2026)

## Abstract (arXiv)

Recent advancements in audio generation have enabled the creation of high-fidelity audio clips from free-form textual descriptions. However, temporal relationships, a critical feature for audio content, are currently underrepresented in mainstream models, resulting in an imprecise temporal controllability. Specifically, users cannot accurately control the timestamps of sound events using free-form text. We acknowledge that a significant factor is the absence of high-quality, temporally-aligned audio-text datasets, which are essential for training models with temporal control. The more temporally-aligned the annotations, the better the models can understand the precise relationship between audio outputs and temporal textual prompts. Therefore, we present a strongly aligned audio-text dataset, AudioTime. It provides text annotations rich in temporal information such as timestamps, duration, frequency, and ordering, covering almost all aspects of temporal control. Additionally, we offer a comprehensive test set and evaluation metric to assess the temporal control performance of various models. Examples are available on the https://zeyuxie29.github.io/AudioTime/