https://github.com/oldhero5/spectrosense
SpectroSense is an open-source RF signal analysis platform that combines artificial intelligence with traditional signal processing techniques to automatically identify and classify radio frequency signals from spectrograms.
https://github.com/oldhero5/spectrosense
ai amateur-radio claude-ai llm machine-learning python rf-signals scipy signal-processing spectral-analysis spectrum-monitoring visualization
Last synced: about 1 month ago
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SpectroSense is an open-source RF signal analysis platform that combines artificial intelligence with traditional signal processing techniques to automatically identify and classify radio frequency signals from spectrograms.
- Host: GitHub
- URL: https://github.com/oldhero5/spectrosense
- Owner: oldhero5
- License: other
- Created: 2024-11-26T10:51:36.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-01-09T11:46:50.000Z (5 months ago)
- Last Synced: 2025-04-13T10:53:07.952Z (about 1 month ago)
- Topics: ai, amateur-radio, claude-ai, llm, machine-learning, python, rf-signals, scipy, signal-processing, spectral-analysis, spectrum-monitoring, visualization
- Language: Python
- Homepage:
- Size: 37.1 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# SpectroSense 📡
SpectroSense is an AI-powered RF signal analysis and classification tool that combines advanced signal processing with large language models to automatically identify and classify radio frequency signals from spectrograms.
## 🚀 Features
- **Multiple AI Models**: Support for both Claude and Meta's Llama Vision model
- **Automated Signal Classification**: Leverages AI to identify RF signals in spectrograms
- **Direct Image Analysis**: Process existing spectrogram images in various formats
- **Batch Processing**: Process multiple recordings or images efficiently
- **Detailed Analysis Reports**: Generate comprehensive JSON reports of identified signals
- **Visualization Tools**: Generate high-quality spectrograms
- **Extensible Architecture**: Easy to add new signal types and analysis methods
## 🛠️ Installation
```bash
pip install spectrosense
```
For development installation:
```bash
git clone https://github.com/oldhero5/spectrosense.git
cd spectrosense
pip install -e ".[dev]"
```
## 🔧 Configuration
1. Copy the example environment file:
```bash
cp .env.example .env
```
2. Edit `.env` with your settings:
```bash
# For Claude
AI_MODEL_TYPE=claude
ANTHROPIC_API_KEY=your_api_key_here
# For Llama
AI_MODEL_TYPE=llama
VLLM_SERVER_URL=http://localhost:8000
```
## 📖 Quick Start
```python
from spectrosense import SpectrogramImageAnalyzer
from spectrosense.ai_integration import ModelType
# Using Claude
analyzer = SpectrogramImageAnalyzer(
model_type=ModelType.CLAUDE,
anthropic_api_key="your-api-key"
)
# Using Llama
analyzer = SpectrogramImageAnalyzer(
model_type=ModelType.LLAMA,
vllm_server_url="http://localhost:8000"
)
# Analyze a single image
result = analyzer.analyze_spectrogram_image("spectrogram.png")
print(result)
# Batch process a directory
results = analyzer.batch_analyze_images("spectrograms/")
```
## 📊 Example Output
```json
{
"signal_types": ["wifi_halow"],
"confidence": "high",
"features": [
"2MHz channel bandwidth",
"Regular packet structure",
"Center frequency: 919MHz"
],
"notes": "IEEE 802.11ah signal with MCS0 modulation"
}
```
## 🚀 Model Setup
### Claude Setup
1. Obtain an API key from Anthropic
2. Set the key in your environment
### Llama Setup
1. Install vLLM:
```bash
pip install vllm
```
2. Start the vLLM server:
```bash
python -m vllm.entrypoints.api_server \
--model meta-llama/Llama-3.2-11B-Vision-Instruct \
--port 8000
```
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.
## 📝 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🎯 Roadmap
- [X] Support for more AI models
- [ ] Real-time signal processing
- [ ] Web interface for analysis
- [ ] GPU acceleration
- [ ] Batch processing optimization
- [ ] Custom model training
## 📚 Documentation
Full documentation is available at [spectrosense.readthedocs.io](https://spectrosense.readthedocs.io)
---
Made with ❤️ by the SpectroSense Team