spectrosense 0.2.1

AI-powered RF signal analysis and classification

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

• Automated Signal Classification: Leverages Claude AI to identify RF signals in spectrograms
• Batch Processing: Process multiple recordings 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

pip install spectrosense

For development installation:

git clone https://github.com/yourusername/spectrosense.git
cd spectrosense
pip install -e ".[dev]"

🔧 Configuration

1. Copy the example environment file:

cp .env.example .env

2. Edit .env with your settings:

ANTHROPIC_API_KEY=your_api_key_here

📖 Quick Start

from spectrosense import SignalProcessor, SpectrogramVisualizer, AIAnalyzer

# Initialize components
processor = SignalProcessor()
visualizer = SpectrogramVisualizer()
analyzer = AIAnalyzer(api_key="your-api-key")

# Process a recording
data = processor.load_signal("recording.wav")
segments, timestamps = processor.generate_spectrogram(data)

# Generate and analyze spectrograms
for segment, timestamp in zip(segments, timestamps):
    image_path = visualizer.save_spectrogram(segment, timestamp)
    analysis = analyzer.analyze_image(image_path)
    print(analysis)

📊 Example Output

{
    "signal_types": ["wifi_halow"],
    "confidence": "high",
    "features": [
        "2MHz channel bandwidth",
        "Regular packet structure",
        "Center frequency: 919MHz"
    ],
    "notes": "IEEE 802.11ah signal with MCS0 modulation"
}

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🎯 Roadmap

• Real-time signal processing
• Web interface for analysis
• Support for more signal types
• GPU acceleration
• Batch processing optimization
• Integration with more LLMs
• Custom model training

📚 Documentation

(In Progress) Full documentation is available at spectrosense.readthedocs.io

🔍 Example Use Cases

• Amateur Radio Signal Analysis
• RF Environment Monitoring
• Protocol Identification
• Spectrum Management
• Research and Education

⚡ Performance

• Processes 1 hour of recordings in ~5 minutes
• Supports files up to 2GB
• 90%+ classification accuracy for common signals

🛟 Support

• 📫 For bugs and features, open an issue
• 💬 For questions, use Discussions

🙏 Acknowledgments

• Anthropic for Claude AI
• SciPy community
• Contributors and testers

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Made with ❤️ by the SpectroSense Team