Update README.md

README.md

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 ---
-license: cc-by-nc-4.0
+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.