spectrosense 0.3.0

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

• 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

pip install spectrosense

For development installation:

git clone https://github.com/oldhero5/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:

# 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

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

{
    "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:

pip install vllm

2. Start the vLLM server:

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 for details.

📝 License

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

🎯 Roadmap

• 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

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Made with ❤️ by the SpectroSense Team