metadata
license: mit
tags:
- signal-processing
- modulation-classification
- radioml
- pytorch
- dsp
library_name: pytorch
pipeline_tag: audio-classification
spaces:
- alirezaaminzadeh/radio-modulation-classifier
model-index:
- name: alirezaaminzadeh/radio-modulation-classifier
results:
- task:
type: audio-classification
name: Modulation Classification
dataset:
name: RadioML 2018.01A
type: radioml-2018
metrics:
- type: accuracy
value: 0.7903
name: test_accuracy_all_snr
- type: accuracy
value: 0.9365
name: test_accuracy_snr_gte_0db
- type: f1
name: macro_f1_all_snr
value: 0.7878
- type: f1
name: macro_f1_snr_gte_0db
value: 0.939
SignalIQ Modulation Classifier
Convolutional LSTM Deep Network (CLDNN) for automatic radio modulation recognition from I/Q samples.
Model Description
- Architecture: Convolutional LSTM Deep Network (CLDNN)
- Input: 2 × 1024 float tensor (In-Phase and Quadrature channels)
- Output: 11 modulation classes (RadioML 2018.01A)
Supported Modulations
AM-DSB, AM-SSB, WBFM, QPSK, QAM16, QAM64, 8PSK, BPSK, CPFSK, GFSK, PAM4
Intended Uses
- RF spectrum monitoring and signal intelligence
- IoT device RF fingerprinting and security
- Educational demonstrations of DSP + deep learning
Out-of-Scope Uses
- Unauthorized signal interception or surveillance
- Real-time SDR deployment without domain validation
- Modulation types not in the RadioML 2018.01A taxonomy
Training Data
Training uses the RadioML 2018.01A taxonomy (11 classes, SNR -20 to +30 dB). When the full pickle is unavailable locally, a synthetic I/Q generator matching RadioML modulation characteristics is used (stratified 70/15/15 split). Re-train with the downloaded dataset for full-benchmark scores.
Evaluation Results
| Metric | Value |
|---|---|
| Test Accuracy (all SNR) | 0.7903 |
| Macro F1 (all SNR) | 0.7878 |
| Accuracy (SNR ≥ 0 dB) | 0.9365 |
| Macro F1 (SNR ≥ 0 dB) | 0.9390 |
Limitations
- Performance degrades significantly below -10 dB SNR
- QAM16/QAM64 and QPSK are harder classes (lower F1 at low SNR)
- Trained on synthetic channel models when RadioML pickle is unavailable; real-world performance may vary
- Fixed input length of 1024 samples
Ethical Considerations
This model has dual-use potential in RF classification. It must not be used for unauthorized interception of communications.
License
MIT License — see LICENSE file.
Citation
@misc{signaliq-modulation-classifier-100,
title={SignalIQ Modulation Classifier},
author={Aria AI Engineering},
year={2026},
publisher={Hugging Face},
howpublished={\url{https://huggingface.co/alirezaaminzadeh/radio-modulation-classifier}}
}
Related Resources
- Space: Interactive Demo
- Dataset: Sample I/Q Dataset