---
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)