---
license: mit
language:
- en
tags:
- audio
- temporal-grounding
- audio-language-modeling
pretty_name: SpotSound-Bench
size_categories:
- n<1K
---

# SpotSound-Bench: A 'Needle-in-a-Haystack' Evaluation for Audio Temporal Grounding

[![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://loiesun.github.io/spotsound/)
[![GitHub](https://img.shields.io/badge/GitHub-Code-black)](https://github.com/LoieSun/spotsound)
[![Paper](https://img.shields.io/badge/arXiv-Paper-red)](https://arxiv.org/abs/2604.13023)

## Benchmark Summary

**SpotSound-Bench** is a challenging temporal grounding benchmark designed to evaluate Large Audio-Language Models (ALMs). 

Existing benchmarks for audio temporal grounding often feature high ratios of target-window duration to full audio clip duration, which fail to simulate real-world scenarios where short events are obscured by dense background sounds. To bridge this gap, we introduce SpotSound-Bench, featuring short acoustic events embedded within long, unstructured recordings. 

This benchmark creates a rigorous **‘needle-in-a-haystack’** evaluation, demanding high temporal precision and robust resistance against hallucinations from audio-language models.

## Benchmark Characteristics

- **Average Clip Length:** 54.2 seconds
- **Average Target Event Length:** 3.9 seconds
- **Temporal Density:** 7.2% (Target event duration / Full audio clip duration)
- **Challenge:** A large search space dominated by background content, requiring models to pinpoint exact timestamps of short events while ignoring complex background ambiance and avoiding hallucinated predictions for non-existent events.

## Data Structure

<pre>
  {
    "audio_path": "_Uro9suV3xU_130_187.wav",
    "caption": "hair dryer drying",
    "annotations": [[8.1, 10.9]]
  },
</pre>

## Citation
If you use this code and data for your research or project, please cite:

    @inproceedings{sun2026spotsound,
        title={SpotSound: Enhancing Large Audio-Language Models with Fine-Grained Temporal Grounding},
        author={Sun, Luoyi and Zhou, Xiao and Li, Zeqian and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
		journal={arXiv preprint arXiv:2604.13023},
	    year={2026}
	}

## Contact
For questions, please contact: loiesun411@gmail.com.