---
license: cc-by-4.0
task_categories:
  - audio-classification
language:
  - en
tags:
  - footstep-detection
  - footstep-audio
  - sound-event-detection
  - audio-classification
  - acoustic-recognition
  - walking-sounds
  - human-activity-recognition
  - smart-home
  - acoustic-biometrics
  - footstep-biometrics
  - person-identification
  - surface-classification
  - foley
  - foley-synthesis
  - environmental-sound
  - real-world-audio
  - WAV
  - audio-dataset
  - field-recordings
  - PAD
size_categories:
  - 1K<n<10K
pretty_name: Footstep Detection Audio Dataset
modality:
  - audio
---
# Footstep Detection Dataset — 50 Hours of Real Footstep Audio

**50 hours of real footstep audio recordings** for training footstep detection, sound event detection, and audio classification models. 166 manually verified files captured in natural indoor and outdoor conditions, with per-file metadata on surface, footwear, location, and background noise. The largest publicly listed footstep audio dataset — 3–5× larger than academic benchmarks (AFPILD: 10h, AFPID-II: 14h).

## Contact us and share your feedback — receive additional samples for free! 😊

## Key Highlights

- **50 hours** of real-world footstep audio
- **166 manually verified files** — every recording reviewed for clear footstep audibility
- **Indoor + outdoor** capture conditions
- **6 surface categories** annotated per file
- **6 footwear categories** annotated per file
- **No synthetic audio, no augmentation, no AI-generated content**
- Smartphone-first recordings (matches real deployment conditions)

## Use This Dataset For

- **Footstep detection** — binary or multi-class footstep classifiers for smart home, security, and IoT
- **Sound event detection (SED)** — footstep as a target class in AudioSet-style models
- **Acoustic person identification** — biometric models recognizing individuals by walking sound
- **Walking surface classification** — distinguishing footsteps across floor materials
- **Activity recognition** — elderly care, fall detection, ambient assisted living
- **Foley generation** — training V2A models for walking sounds in games and animation

## Dataset Structure

```
footstep-detection-dataset/
├── audio/
│   ├── rec_001.wav
│   ├── rec_002.wav
│   └── ... (158 WAV + 8 M4A files)
├── metadata.csv
└── README.md
```

### metadata.csv schema

| Field | Type | Values |
|-------|------|--------|
| `file_id` | string | unique recording ID |
| `filename` | string | path to audio file |
| `duration_sec` | float | 10–100 seconds |
| `sample_rate` | int | 48000 (majority), 44100, 16000 |
| `channels` | int | 1 (mono) or 2 (stereo) |
| `format` | string | wav, m4a |
| `surface` | string | wood_laminate, tile, carpet, concrete_asphalt, stairs, other |
| `footwear` | string | barefoot, slippers, sandals, sneakers, dress_shoes_boots, other |
| `location` | string | indoor, outdoor |
| `noise_level` | string | low, medium, high |
| `device_class` | string | smartphone, laptop, tablet |

## Dataset Statistics

| Metric | Value |
|--------|-------|
| Total duration | 50 hours |
| Total files | 166 |
| WAV files | 158 |
| M4A files | 8 |
| File duration range | 10–100 sec |
| Sample rates | 48 kHz / 44.1 kHz / 16 kHz |
| Surface categories | 6 |
| Footwear categories | 6 |
| Capture conditions | indoor + outdoor |

## How This Compares to Academic Footstep Audio Datasets

| Dataset | Duration | Footstep samples | Metadata |
|---------|----------|------------------|----------|
| **Axon Labs Footstep Detection** | **50 hours** | **166 files** | **Surface + footwear + noise + location** |
| AFPILD | 10 hours | 40 subjects | Location only |
| AFPID-II | 14 hours | 41 subjects | Clothing + shoes |
| FSD50K | <1h equivalent | 921 samples | None (label only) |
| ESC-50 | <0.1h equivalent | 40 samples | None (label only) |
| PURE | 14 minutes | 14 samples | 5 subjects |

## Quick Start — Loading with 🤗 Datasets

```python
from datasets import load_dataset

dataset = load_dataset("AxonData/footstep-detection-dataset")
print(dataset)

sample = dataset["train"][0]
print(sample["audio"])           # audio array + sampling_rate
print(sample["surface"])         # e.g. "wood_laminate"
print(sample["footwear"])        # e.g. "sneakers"
print(sample["noise_level"])     # e.g. "low"
```

## Quick Start — PyTorch DataLoader

```python
import torch
import torchaudio
from datasets import load_dataset

ds = load_dataset("AxonData/footstep-detection-dataset", split="train")

def collate(batch):
    waveforms = [torch.tensor(item["audio"]["array"]) for item in batch]
    labels = [item["surface"] for item in batch]
    return waveforms, labels

loader = torch.utils.data.DataLoader(ds, batch_size=8, collate_fn=collate)
```

## Sample vs Full Version

This HuggingFace repository contains a **sample subset** for evaluation. The full 50-hour dataset is licensed for commercial use through Axon Labs.

**Full version of dataset is available for commercial usage — leave a request on our website [Axonlabs](https://axonlab.ai/dataset/footsteps-audio-dataset/?utm_source=hugging-face&utm_medium=cpc&utm_campaign=footstep&utm_content=readme) to purchase the dataset 💰**

## What Makes This Dataset Unique

- **Largest footstep audio corpus available commercially** — 3–5× larger than the most cited academic alternatives
- **Manually verified, not scraped** — every file reviewed for clear footstep audibility
- **Real smartphone recordings** — matches deployment conditions for smart speakers, phones, wearables
- **Structured metadata across 4 dimensions** — supports filtered training and multi-task learning
- **Backed by a biometric AI specialist** — Axon Labs builds datasets used by 21% of iBeta 2025 certified companies

## Two Dataset Versions Available

- **Sample Version** — open subset for EDA, evaluation, and proof-of-concept (this repo)
- **Full Version** — 50 hours of audio with complete metadata, licensed for commercial training

[Contact us](https://axonlab.ai/dataset/footsteps-audio-dataset/) to choose the version that fits your project.

## FAQ

**Q: What's the largest publicly available footstep audio dataset?**
This one — 50 hours of curated recordings, 3–5× larger than AFPILD (10h) or AFPID-II (14h), which are the most cited academic benchmarks in the field. Sound event datasets like FSD50K and ESC-50 contain footsteps only as a small subset (under 1,000 samples).

**Q: Can I use this dataset for footstep biometrics / acoustic person identification?**
Yes. The dataset is well-suited for footstep biometrics research, especially as a pre-training corpus. For per-subject identification tasks, we can collect additional per-subject sessions on request through our custom data collection service.

**Q: What surfaces and footwear are covered?**
6 surface types (wood/laminate, tile, carpet, concrete/asphalt, stairs, other) and 6 footwear types (barefoot, slippers, sandals, sneakers, dress shoes/boots, other). Every file is labeled across both dimensions.

**Q: Is the data ethically collected?**
Yes. All recordings were captured with explicit participant consent and processed in accordance with GDPR. Full documentation of consent and provenance is available for the commercial version.

## Citation

If you use this dataset in your research, please cite:

```bibtex
@misc{axonlabs2026footstep,
  title  = {Footstep Detection Audio Dataset},
  author = {Axon Labs},
  year   = {2026},
  url    = {https://axonlab.ai/dataset/footsteps-audio-dataset/}
}
```

---

**keywords**: footstep audio dataset, footstep sound dataset, footstep detection dataset, sound event detection, audio classification dataset, acoustic person identification, footstep biometrics, walking surface classification, foley dataset, environmental sound dataset, real-world audio dataset, smart home audio, activity recognition

Visit us at [**Axonlabs**](https://axonlab.ai/?utm_source=hugging-face&utm_medium=cpc&utm_campaign=footstep&utm_content=footer) to request a full version of the dataset for commercial usage.