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SCRIBE - Sonic Resonance Intelligence and Behavioral Exploration

Advanced acoustic intelligence platform using active sonic sensing for material analysis and structural health monitoring. Employs AI-powered pattern recognition to identify materials, detect structural anomalies, and create detailed environmental maps through resonance analysis. Features real-time processing, machine learning capabilities, and RESTful API for seamless integration. Built with Python, FastAPI, NumPy, and LibROSA. Ideal for construction, manufacturing, aerospace, and research applications requiring non-destructive testing. Open source with comprehensive documentation and developer guides. Revolutionizing material analysis through cutting-edge acoustic technology and intelligent pattern recognition systems.

Quick Start

Prerequisites

  • Python 3.8+
  • NumPy
  • LibROSA
  • FastAPI

Installation

git clone https://github.com/AutoBotSolutions/SCRIBE-Sonic-Resonance-Intelligence-and-Behavioral-Exploration.git
cd SCRIBE-Sonic-Resonance-Intelligence-and-Behavioral-Exploration
pip install -r requirements.txt

Running the System

# Start API server
./start_api.sh

# Start interactive mode
./start_interactive.sh

# System validation
python3 validate_system.py

Documentation

Complete documentation is available at: https://autobotsolutions.github.io/SCRIBE-Sonic-Resonance-Intelligence-and-Behavioral-Exploration/

Features

  • Real-time Material Analysis: Instant identification through acoustic signatures
  • Structural Health Monitoring: Non-destructive testing and anomaly detection
  • AI-Powered Learning: Adaptive system that improves with each analysis
  • RESTful API: Complete programmatic access
  • Multi-frequency Support: Various frequency ranges and signal types
  • Interactive CLI: Command-line interface for direct interaction

🏗️ Architecture

Built with a scalable microservices architecture:

  • Core Engine: Signal processing and analysis
  • AI Module: Machine learning and pattern recognition
  • API Layer: FastAPI-based REST interface
  • Storage: Learning database and configuration management

📊 API Usage

import requests

# Perform material analysis
response = requests.post('http://localhost:8000/scan', json={
    'signal_type': 'sine',
    'frequency': 440.0,
    'duration': 2.0
})

result = response.json()
print(f"Material identified: {result['material']}")

Configuration

Edit config.json to customize:

  • Signal parameters
  • Analysis thresholds
  • API settings
  • Learning preferences

Contributing

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

License

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

Contact

Acknowledgments

  • NumPy team for numerical computing
  • LibROSA for audio analysis
  • FastAPI for web framework
  • Open source community for inspiration and support

© 2026 Robert Trenaman | Software Customs (Auto Bot Solution) | All Rights Reserved