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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.19295 |
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| _version_ | 1866911172249255936 |
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| author | Kim, Yonghyun Han, Chaeyeon Sarode, Akash Posner, Noah Guhathakurta, Subhrajit Lerch, Alexander |
| author_facet | Kim, Yonghyun Han, Chaeyeon Sarode, Akash Posner, Noah Guhathakurta, Subhrajit Lerch, Alexander |
| contents | Audio-based pedestrian detection is a challenging task and has, thus far, only been explored in noise-limited environments. We present a new dataset, results, and a detailed analysis of the state-of-the-art in audio-based pedestrian detection in the presence of vehicular noise. In our study, we conduct three analyses: (i) cross-dataset evaluation between noisy and noise-limited environments, (ii) an assessment of the impact of noisy data on model performance, highlighting the influence of acoustic context, and (iii) an evaluation of the model's predictive robustness on out-of-domain sounds. The new dataset is a comprehensive 1321-hour roadside dataset. It incorporates traffic-rich soundscapes. Each recording includes 16kHz audio synchronized with frame-level pedestrian annotations and 1fps video thumbnails. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19295 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Audio-Based Pedestrian Detection in the Presence of Vehicular Noise Kim, Yonghyun Han, Chaeyeon Sarode, Akash Posner, Noah Guhathakurta, Subhrajit Lerch, Alexander Audio and Speech Processing Artificial Intelligence Machine Learning Sound Audio-based pedestrian detection is a challenging task and has, thus far, only been explored in noise-limited environments. We present a new dataset, results, and a detailed analysis of the state-of-the-art in audio-based pedestrian detection in the presence of vehicular noise. In our study, we conduct three analyses: (i) cross-dataset evaluation between noisy and noise-limited environments, (ii) an assessment of the impact of noisy data on model performance, highlighting the influence of acoustic context, and (iii) an evaluation of the model's predictive robustness on out-of-domain sounds. The new dataset is a comprehensive 1321-hour roadside dataset. It incorporates traffic-rich soundscapes. Each recording includes 16kHz audio synchronized with frame-level pedestrian annotations and 1fps video thumbnails. |
| title | Audio-Based Pedestrian Detection in the Presence of Vehicular Noise |
| topic | Audio and Speech Processing Artificial Intelligence Machine Learning Sound |
| url | https://arxiv.org/abs/2509.19295 |