README.md

---
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

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

## Key Highlights

- **50 hours** of real-world footstep audio
- **Indoor + outdoor** capture conditions
- **Different surface categories** annotated per file
- **Different 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 Statistics

| Metric | Value |
|--------|-------|
| Total duration | 50 hours |
| File duration range | 10–100 sec |
| Sample rates | 48 kHz / 44.1 kHz / 16 kHz |
| 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 |

**Full version of dataset is available for commercial usage — leave a request on our website [Axonlabs](https://axonlab.ai/?utm_source=hugging-face&utm_medium=cpc&utm_campaign=profile&utm_content=profile_link) 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** - supports filtered training and multi-task learning

[Contact us](https://axonlab.ai/?utm_source=hugging-face&utm_medium=cpc&utm_campaign=profile&utm_content=profile_link) to choose the version that fits your project.

## FAQ

**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.

**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=profile&utm_content=profile_link) to request a full version of the dataset for commercial usage