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Robert Trenaman edited this page May 7, 2026 · 2 revisions

SCRIBE System Components

Component Overview

The SCRIBE system consists of 7 core components, each with specific responsibilities and well-defined interfaces. This section provides detailed documentation for each component.

Component Structure

src/
├── core/                    # System orchestration
├── emitter/                 # Audio signal generation
├── listener/                # Audio capture
├── processing/              # Signal analysis
├── ai/                      # AI interpretation
├── feedback/                # Learning system
├── chat/                    # User interface
├── api/                     # REST API
├── monitoring/              # Analytics
└── utils/                   # Utilities

System Controller

File: src/core/system_controller.py

Purpose: Central orchestration and component coordination

Key Methods:

  • start() - Initialize all components
  • stop() - Cleanup and shutdown
  • perform_resonance_scan() - Execute complete scan cycle
  • get_system_status() - Return system health status
  • get_scan_history() - Retrieve scan history

Dependencies:

  • All core components
  • Configuration system
  • Logging framework

Usage Example:

from core.system_controller import ScribeSystemController
from utils.config import Config

config = Config()
system = ScribeSystemController(config)

await system.start()
result = await system.perform_resonance_scan()
await system.stop()

🔊 Resonance Emission Engine

Files: src/emitter/

Purpose: Generate controlled acoustic signals for environmental probing

Real Engine: tone_generator.py

Signal Types:

  • Sine waves (single frequency)
  • Frequency sweeps (20Hz - 20kHz)
  • Pulse bursts
  • Harmonic stacks

Key Methods:

  • emit_signals() - Generate audio signals
  • initialize() - Setup audio hardware
  • cleanup() - Release resources

Mock Engine: mock_audio.py

Purpose: Fallback for testing without audio hardware

Features:

  • Simulated signal generation
  • Realistic timing and metadata
  • No hardware dependencies

🎙️ Micro Listening Module

Files: src/listener/

Purpose: Capture environmental acoustic responses

Real Module: mic_capture.py

Features:

  • High-fidelity audio capture
  • Real-time processing
  • Multi-device support

Mock Module: mock_capture.py

Purpose: Fallback for testing without audio hardware

Features:

  • Simulated audio capture
  • Realistic response generation
  • Configurable noise and reflections

Signal Processing Layer

File: src/processing/fft_analyzer.py

Purpose: Extract meaningful features from audio signals

Analysis Types:

  • FFT (Fast Fourier Transform)
  • Spectrogram analysis
  • Envelope detection
  • Resonance peak extraction
  • Harmonic analysis
  • Noise analysis

Key Methods:

  • analyze_signal() - Complete signal analysis
  • compute_fft() - FFT computation
  • _analyze_spectrogram() - Spectrogram generation
  • _analyze_resonance_peaks() - Peak detection

Output Features:

  • Time domain features (RMS, peak, zero crossings)
  • Frequency domain features (dominant frequencies, spectral centroid)
  • Resonance peaks and Q-factors
  • Harmonic content and ratios
  • Envelope characteristics

AI Interpretation Engine

File: src/ai/interpreter.py

Purpose: Intelligent pattern recognition and interpretation

Approaches:

  • Rule-based analysis
  • Machine learning pattern matching
  • Anomaly detection
  • Confidence scoring

Key Methods:

  • interpret_resonance() - Main interpretation function
  • _rule_based_analysis() - Apply expert rules
  • _ml_pattern_matching() - ML-based recognition
  • _detect_anomalies() - Anomaly detection

Output Categories:

  • Material identification
  • Environment classification
  • State assessment
  • Anomaly detection
  • Confidence scores

Feedback Loop System

File: src/feedback/learning_system.py

Purpose: Continuous learning and adaptation from user feedback

Features:

  • User feedback integration
  • Pattern adaptation
  • Learning insights
  • Performance tracking
  • Database storage

Key Methods:

  • store_scan_result() - Store scan data
  • add_user_feedback() - Process user corrections
  • get_learning_insights() - Learning analytics
  • adapt_patterns() - Update recognition patterns

Database Schema:

  • Scan results storage
  • User feedback tracking
  • Pattern adaptations
  • Performance metrics

Chat Interface

File: src/chat/interface.py

Purpose: Natural language user interaction

Features:

  • Command processing
  • Natural language queries
  • Real-time responses
  • Context awareness

Supported Commands:

  • /scan - Perform resonance analysis
  • /status - Check system health
  • /help - Show available commands
  • /history - View scan history
  • /feedback - Provide corrections

Natural Language Examples:

  • "What did you detect?"
  • "Is this environment stable?"
  • "Compare this scan to previous"
  • "What changed?"

REST API

File: src/api/main.py

Purpose: HTTP interface for external integration

Endpoints:

  • GET / - API information
  • POST /scan - Perform scan
  • GET /status - System status
  • GET /scans - Scan history
  • POST /feedback - User feedback
  • GET /learning/insights - Learning analytics

Features:

  • FastAPI framework
  • Automatic documentation
  • Request validation
  • Error handling

Monitoring & Analytics

File: src/monitoring/analytics.py

Purpose: Real-time system monitoring and performance tracking

Features:

  • Prometheus metrics
  • Performance tracking
  • System health monitoring
  • Alert generation

Metrics Collected:

  • Scan count and duration
  • Confidence scores
  • Error rates
  • Resource usage
  • User interaction patterns

Utilities

Configuration: src/utils/config.py

Purpose: System configuration management

Classes:

  • AudioConfig - Audio settings
  • ProcessingConfig - Signal processing parameters
  • AIConfig - AI model settings
  • DatabaseConfig - Database configuration

Logging: src/utils/logger.py

Purpose: Centralized logging system

Features:

  • Console and file logging
  • Rotating log files
  • Component-specific loggers
  • Error tracking

Component Integration

Data Flow

Emission Engine → Listening Module → Signal Processing → AI Interpreter → Feedback Loop → Chat Interface

Communication Patterns

  • Async/Await: Non-blocking operations
  • Event-driven: Component notifications
  • Status polling: Health checks
  • Error propagation: Graceful failure handling

Dependencies

  • Core: All components depend on system controller
  • Audio: Emission and listener are independent
  • Processing: Depends on audio components
  • AI: Depends on processing results
  • Feedback: Depends on AI interpretation
  • Interface: Depends on all components

Component Development

Adding New Components

  1. Create component directory
  2. Implement required interfaces
  3. Add to system controller
  4. Update configuration
  5. Add tests
  6. Update documentation

Component Interfaces

  • initialize() - Setup component
  • cleanup() - Release resources
  • get_status() - Health check
  • Error handling and logging

Best Practices

  • Async/await for I/O operations
  • Proper error handling
  • Comprehensive logging
  • Configuration flexibility
  • Mock implementations for testing

Last Updated: 2026-05-06
Component Version: 1.0.0
Status: Production Ready

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