---
license: cc-by-4.0
task_categories:
- audio-classification
size_categories:
- n>1T
tags:
- pedestrian detection
---
# ASPED: An Audio Dataset for Detecting Pedestrians

This repo contains the data for the ASPED dataset, presented at ICASSP 2024.
- [Paper Link](https://arxiv.org/abs/2309.06531), [Project Homepage](https://urbanaudiosensing.github.io/ASPED.html)

- Pavan Seshadri, Chaeyeon Han, Bon-Woo Koo, Noah Posner, Suhbrajit Guhathakurta, Alexander Lerch

## Usage
This dataset contains audio and video recordings of pedestrian activity collected at various locations in and around Georgia Tech. 

Labels of pedestrian counts per each second of audio/video are provided as well, calculated via a computer vision model (Mask2Former trained on msft-coco) using the video recordings.
### Access
It is recommended to use the huggingface_hub library to download the dataset from this location. [Info on downloading with huggingface_hub](https://huggingface.co/docs/huggingface_hub/guides/download).

Downloading the entire dataset can be done with the following code:
```
from huggingface_hub import snapshot_download
snapshot_download(repo_id="pseshadri9/ASPED", repo_type="dataset")
```
Alternatively if you would like to download only the audio or video, pass the ignore_patterns flag to snapshot_download to avoid downloading the entire set. 

**Audio Only**
```
from huggingface_hub import snapshot_download
snapshot_download(repo_id="pseshadri9/ASPED", repo_type="dataset", ignore_patterns="*.mp4")
```

**Video Only**

```
from huggingface_hub import snapshot_download
snapshot_download(repo_id="pseshadri9/ASPED", repo_type="dataset", ignore_patterns="*.flac")
```


## Citation 
```
@inproceedings{Seshadri24,
  title={ASPED: An Audio Dataset for Detecting Pedestrians},
  author={Seshadri, Pavan and Han, Chaeyeon and Koo, Bon-Woo and Posner, Noah and Guhathakurta, Suhbrajit and Lerch, Alexander},
  booktitle={Proc. of ICASSP 2024},
  pages={1--5},
  year={2024},
  organization={IEEE}
}
```