| --- |
| license: mit |
| task_categories: |
| - time-series-forecasting |
| - audio-classification |
| language: |
| - en |
| tags: |
| - rf-signals |
| - radio |
| - rtl-sdr |
| - signal-processing |
| - machine-learning |
| - telecommunications |
| - software-defined-radio |
| pretty_name: RTL-ML RF Signal Classification Dataset v2 |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # RTL-ML Dataset v2 |
|
|
| ## Dataset Summary |
|
|
| This dataset contains **800 validated RF signal samples** captured using an RTL-SDR Blog V4 dongle on an Indiedroid Nova (RK3588S). Designed for training machine learning models to classify common RF signals. |
|
|
| **Samples:** 800 (7 classes) |
| **Format:** NumPy arrays (.npy files) — each file is a dict with IQ data + metadata |
| **Sample Rate:** 1.024 MSPS |
| **Sample Duration:** 0.5 seconds per capture |
| **Quality Gates:** DC removal, auto-gain, 6 dB minimum SNR, per-class validation |
|
|
| ## Signal Classes |
|
|
| | Class | Frequency | Count | Description | |
| |-------|-----------|-------|-------------| |
| | FM_broadcast | 88.5, 93.3, 98.7, 101.1, 105.7 MHz | 200 | Commercial FM radio (5 stations) | |
| | NOAA_weather | 162.4 MHz | 100 | Weather radio broadcasts | |
| | APRS | 144.39 MHz | 100 | Amateur radio position reporting | |
| | pager | 152.84 MHz | 100 | POCSAG pager transmissions | |
| | ISM_sensors | 433.92 MHz | 100 | Wireless sensors & remote controls | |
| | FRS_GMRS | 462.5625 MHz | 100 | Family/general mobile radio | |
| | noise | 145.0 MHz | 100 | Background RF noise baseline | |
|
|
| ## What Changed from v1 |
|
|
| - **7 classes** (removed ADS-B — 1090 MHz out of R828D tuner range; removed NOAA APT — decommissioned Aug 2025; added FRS/GMRS) |
| - **800 samples** (up from 240) with 100+ per class |
| - **DC offset removal** on every capture |
| - **Auto-gain calibration** per frequency |
| - **6 dB SNR gate** — rejects weak/empty captures |
| - **Per-class quality validators** (bandwidth, burst ratio, packet detection) |
| - **Temporal train/test split** — first 80% train, last 20% test (no data leakage) |
| - **Multi-frequency FM** — trained on 5 stations for frequency-invariant classification |
| - **Metadata in every file** — center_freq, sample_rate, timestamp, label, snr_db, version |
| |
| ## Model Performance |
| |
| - **Random Forest:** 96.9% accuracy (155/160 test samples correct) |
| - **Temporal split:** No data leakage between train and test |
| - **Cross-frequency FM:** Generalizes to unseen FM stations |
| |
| ## Sample Format |
| |
| Each .npy file contains a dict: |
| ```python |
| { |
| 'samples': np.array([...], dtype=complex64), # IQ data |
| 'center_freq': 98700000.0, |
| 'sample_rate': 1024000.0, |
| 'timestamp': '2026-01-15T14:23:01', |
| 'label': 'FM_broadcast', |
| 'duration': 0.5, |
| 'snr_db': 17.5, |
| 'version': 'v2' |
| } |
| ``` |
| |
| ## Usage |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| import numpy as np |
| |
| # Download entire dataset |
| dataset_path = snapshot_download( |
| repo_id="TrevTron/rtl-ml-dataset", |
| repo_type="dataset" |
| ) |
| |
| # Load a sample |
| data = np.load(f"{dataset_path}/datasets_validated/FM_broadcast/FM_broadcast_0.npy", allow_pickle=True).item() |
| print(f"Signal: {data['label']}, SNR: {data['snr_db']:.1f} dB, Freq: {data['center_freq']/1e6:.1f} MHz") |
| ``` |
|
|
| ## Dataset Structure |
|
|
| ``` |
| rtl-ml-dataset/ |
| └── datasets_validated/ |
| ├── FM_broadcast/ (200 files from 5 frequencies) |
| ├── NOAA_weather/ (100 files) |
| ├── APRS/ (100 files) |
| ├── pager/ (100 files) |
| ├── ISM_sensors/ (100 files) |
| ├── FRS_GMRS/ (100 files) |
| └── noise/ (100 files) |
| ``` |
|
|
| ## Hardware |
|
|
| - **SDR:** RTL-SDR Blog V4 ($39.95) — **requires [RTL-SDR Blog driver fork](https://github.com/rtlsdrblog/rtl-sdr-blog)** for R828D tuner support |
| - **Computer:** Indiedroid Nova 16GB ($179.95) |
| - **Antenna:** Telescopic dipole (included with V4) |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{rtl-ml-dataset-v2, |
| author = {TrevTron}, |
| title = {RTL-ML Dataset v2: Validated RF Signal Captures}, |
| year = {2026}, |
| publisher = {Hugging Face}, |
| howpublished = {\url{https://huggingface.co/datasets/TrevTron/rtl-ml-dataset}} |
| } |
| ``` |
|
|
| ## License |
|
|
| MIT License — Free for commercial and non-commercial use. |
|
|
| ## Related |
|
|
| - **Code:** [github.com/TrevTron/rtl-ml](https://github.com/TrevTron/rtl-ml) |
| - **Blog:** [unland.dev/blog/building-ai-radio-scanner-rtl-sdr-machine-learning](https://unland.dev/blog/building-ai-radio-scanner-rtl-sdr-machine-learning) |
|
|
