AxonData/footstep-detection-dataset

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

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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 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 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 to request a full version of the dataset for commercial usage