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Datasets:
weswebunited
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rtl-ml-dataset

Tasks:
Time Series Forecasting
Audio Classification
Languages:
English
Size:
100K<n<1M
Tags:
rf-signals
radio
rtl-sdr
signal-processing
machine-learning
telecommunications
License:
Dataset card Files
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Duplicate from TrevTron/rtl-ml-dataset

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Co-authored-by: Trevor Unland <TrevTron@users.noreply.huggingface.co>

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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lz4 filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ # Audio files - uncompressed
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+ *.sam filter=lfs diff=lfs merge=lfs -text
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+ *.raw filter=lfs diff=lfs merge=lfs -text
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+ # Audio files - compressed
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+ # Image files - compressed
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+ ---
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+ license: mit
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+ task_categories:
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+ - time-series-forecasting
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+ - audio-classification
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+ language:
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+ - en
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+ tags:
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+ - rf-signals
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+ - radio
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+ - rtl-sdr
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+ - signal-processing
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+ - machine-learning
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+ - telecommunications
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+ - software-defined-radio
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+ pretty_name: RTL-ML RF Signal Classification Dataset
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+ size_categories:
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+ - 100K<n<1M
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+ ---
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+
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+ # RTL-ML Dataset
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+
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+ ## Dataset Summary
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+
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+ This dataset contains 240 validated RF signal samples captured using an RTL-SDR Blog V4 dongle. It's designed for training machine learning models to classify common RF signals.
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+
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+ **Total Size:** 1.9 GB
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+ **Samples:** 240 (30 samples × 8 classes)
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+ **Format:** NumPy arrays (.npy files)
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+ **Sample Rate:** 1.024 MSPS
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+ **Sample Duration:** 1 second per capture
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+
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+ ## Signal Classes
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+
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+ | Class | Frequency | Count | Description |
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+ |-------|-----------|-------|-------------|
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+ | ADS_B | 1090 MHz | 30 | Aircraft transponder signals |
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+ | APRS | 144.39 MHz | 30 | Amateur radio position reporting |
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+ | FM_broadcast | 88-108 MHz | 30 | Commercial FM radio stations |
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+ | ISM_sensors | 433.92 MHz | 30 | Wireless sensors & remote controls |
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+ | NOAA_APT | 137.5 MHz | 30 | Weather satellite imagery |
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+ | NOAA_weather | 162.4 MHz | 30 | Weather radio broadcasts |
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+ | noise | Various | 30 | Background RF noise baseline |
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+ | pager | 931.9375 MHz | 30 | POCSAG pager transmissions |
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+
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+ ## Validation Metrics
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+
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+ - **ISM Sensors:** 20.6x burst ratio (strong on/off keying)
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+ - **NOAA Weather:** 14.4 dB SNR (clear signal)
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+ - **Pager/APRS:** 12.7 dB SNR (good quality)
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+ - **Model Accuracy:** 87.5% on test set
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+
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+ ## Usage
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+
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+ ```python
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+ from huggingface_hub import snapshot_download
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+ import numpy as np
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+
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+ # Download entire dataset
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+ dataset_path = snapshot_download(
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+ repo_id="TrevTron/rtl-ml-dataset",
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+ repo_type="dataset"
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+ )
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+
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+ # Load a sample
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+ sample = np.load(f"{dataset_path}/datasets_validated/ADS_B_0.npy")
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+ print(f"Signal shape: {sample.shape}") # (1048576,) complex64
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+ ```
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+
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+ ## Dataset Structure
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+
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+ ```
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+ rtl-ml-dataset/
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+ └── datasets_validated/
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+ ├── ADS_B_0.npy ... ADS_B_29.npy (30 files)
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+ ├── APRS_0.npy ... APRS_29.npy (30 files)
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+ ├── FM_broadcast_0.npy ... _29.npy (30 files)
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+ ├── ISM_sensors_0.npy ... _29.npy (30 files)
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+ ├── NOAA_APT_0.npy ... NOAA_APT_29.npy (30 files)
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+ ├── NOAA_weather_0.npy ... _29.npy (30 files)
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+ ├── noise_0.npy ... noise_29.npy (30 files)
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+ └── pager_0.npy ... pager_29.npy (30 files)
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+ ```
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+
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+ Each `.npy` file contains:
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+ - **Shape:** (1048576,) - 1 second @ 1.024 MSPS
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+ - **Dtype:** `complex64` (I/Q samples)
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+ - **Size:** ~8.4 MB per file
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+
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+ ## Hardware
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+
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+ - **SDR:** RTL-SDR Blog V4 ($39.95)
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+ - **Computer:** Indiedroid Nova 16GB ($179.95)
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+ - **Antenna:** Telescopic dipole (included)
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+
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+ ## Model Performance
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+
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+ When trained with Random Forest (100 trees):
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+ - **Overall Accuracy:** 87.5%
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+ - **Perfect Classes:** ADS-B, FM, ISM, NOAA APT, Weather, Pager (100%)
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+ - **Challenging:** APRS ↔ Noise confusion (sparse packets)
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @misc{rtl-ml-dataset,
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+ author = {TrevTron},
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+ title = {RTL-ML Dataset: Validated RF Signal Captures},
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+ year = {2026},
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+ publisher = {Hugging Face},
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+ howpublished = {\url{https://huggingface.co/datasets/TrevTron/rtl-ml-dataset}}
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+ }
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+ ```
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+
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+ ## License
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+
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+ MIT License - Free for commercial and non-commercial use.
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+
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+ ## Related
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+
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+ - **Code:** [github.com/TrevTron/rtl-ml](https://github.com/TrevTron/rtl-ml)
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+ - **Blog:** [unland.dev](https://unland.dev) (coming soon)
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+ - **Hardware Guide:** [Indiedroid Nova Setup](https://github.com/TrevTron/rtl-ml/blob/main/docs/HARDWARE_SETUP.md)
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+
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+ ## Contributions
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+
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+ Captured in Temecula, CA (Southern California) using:
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+ - Clear line of sight to multiple signal sources
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+ - Validated with spectral analysis and manual inspection
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+ - All samples meet minimum SNR requirements (>10 dB for modulated signals)
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+
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+ For questions or improvements, see the [GitHub repository](https://github.com/TrevTron/rtl-ml).
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