Update README.md

README.md

@@ -33,17 +33,16 @@ modality:
 ---
 # 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).
+**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
-- **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
+- **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)
 
@@ -56,46 +55,13 @@ modality:
 - **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
@@ -109,64 +75,19 @@ footstep-detection-dataset/
 | 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 💰**
+**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 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
+- **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
 
-- **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.
+[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: 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.
 
@@ -176,21 +97,6 @@ Yes. The dataset is well-suited for footstep biometrics research, especially as
 **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.
+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