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

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

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

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.