# Audio-Based Pedestrian Detection in the Presence of Vehicular Noise

arXiv: https://arxiv.org/abs/2509.19295 · HTML: https://mlsanity.com/paper/2509.19295/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 5 of 5)

- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [Chatty Maps: Constructing sound maps of urban areas from social media data](https://mlsanity.com/paper/1603.07813/) (2016)
- [Urban Rhapsody: Large-scale exploration of urban soundscapes](https://mlsanity.com/paper/2205.13064/) (2022)
- [ASPED: An Audio Dataset for Detecting Pedestrians](https://mlsanity.com/paper/2309.06531/) (2023)
- [Understanding Pedestrian Movement Using Urban Sensing Technologies: The Promise of Audio-based Sensors](https://mlsanity.com/paper/2406.09998/) (2024)

## Built on by (most cited first, 1 of 1)

- [Cross-Modal Knowledge Distillation for Acoustic Pedestrian Detection](https://mlsanity.com/paper/2609.31785/) (2026)

## Abstract (arXiv)

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.