| --- |
| license: cc-by-nc-4.0 |
| language: |
| - zh |
| - en |
| viewer: false |
| --- |
| [](https://arxiv.org/abs/2604.22245) |
| # LAT-Bench |
| LAT-Bench is the first benchmark designed for evaluating **temporal awareness in long-form audio understanding**. |
| Unlike existing benchmarks limited to short clips, LAT-Bench supports **audio durations up to 30 minutes**, enabling evaluation under realistic long-form scenarios. |
|
|
| The benchmark covers three core tasks: |
| - **Dense Audio Captioning (DAC)**: generate temporally grounded descriptions over the full audio |
| - **Temporal Audio Grounding (TAG)**: localize relevant time spans for a given query |
| - **Targeted Audio Captioning (TAC)**: produce descriptions for specific temporal segments |
|
|
| LAT-Bench contains approximately **40 hours of long-form audio**, including: |
| - **25 hours in Chinese** |
| - **15 hours in English** |
|
|
| The dataset spans diverse real-world scenarios, including conversations, lifestyle vlogs, educational content, and so on. |
|
|
| ## Data Distribution |
|
|
| <p align="center"> |
| <img src="./Figures/bench-figure.png" width="600"/> |
| <em>Figure 1: Duration and scenario distributions of LAT-Bench across Chinese and English.</em> |
| </p> |
| LAT-Bench exhibits balanced coverage across duration ranges and scenarios, ensuring robust evaluation under diverse long-form settings. |
|
|
| <p align="center"> |
| <em>Table 1: Temporal annotation statistics of LAT-Bench across DAC, TAG, and TAC tasks.</em> |
| <img src="./Figures/bench-table.png" width="600"/> |
| </p> |
| The annotations provide comprehensive temporal coverage across the beginning, middle, and end of audio sequences. |
|
|
| ## Data Organization |
|
|
| LAT-Bench is organized into two types of files: **metadata files** and **task files**. |
|
|
| ### Metadata Files |
|
|
| - `./meta/bench-CN-meta.jsonl` |
| - `./meta/bench-EN-meta.jsonl` |
|
|
| These files provide metadata for each audio sample, including: |
| - `id`: unique identifier |
| - `url`: source link for downloading the audio |
| - `title`: original audio title |
| - `duration`: duration in seconds |
|
|
| ### Task Files |
| Dense Audio Captioning (DAC) |
| - `./task/bench-CN-DAC.jsonl` |
| - `./task/bench-EN-DAC.jsonl` |
| |
| Temporal Audio Grounding (TAG) |
| - `./task/bench-CN-TAG.jsonl` |
| - `./task/bench-EN-TAG.jsonl` |
| |
| Targeted Audio Captioning (TAC) |
| - `./task/bench-CN-TAC.jsonl` |
| - `./task/bench-EN-TAC.jsonl` |
|
|
| Each task file contains benchmark instances in a unified format. |
| The audios field references the corresponding audio sample using the id from metadata files. |
|
|
| ## Evaluation Protocol |
|
|
| For detailed evaluation protocols and metrics, please refer to the official repository: |
| 👉 https://github.com/alanshaoTT/LAT-Audio-Repo |
|
|
| ## Citation |
|
|
| If you find this work useful, please cite: |
|
|
| ```bibtex |
| @article{shao2026lataudio, |
| title={Listening with Time: Precise Temporal Awareness for Long-Form Audio Understanding}, |
| author={Shao, Mingchen and Su, Hang and Tian, Wenjie and Mu, Bingshen and Lin, Zhennan and Fan, Lichun and Luo, Zhenbo and Luan, Jian and Xie, Lei}, |
| journal={arXiv preprint arXiv:2604.22245}, |
| year={2026} |
| } |
| ``` |
|
|
| ## Contact |
|
|
| For questions, feedback, or collaboration inquiries, please contact: |
|
|
| 📧 mcshao@mail.nwpu.edu.cn |
