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architecture
Robert Trenaman edited this page May 7, 2026
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The SCRIBE Resonance AI System is built on a modular, event-driven architecture that enables real-time acoustic resonance analysis and intelligent interpretation. The system follows a layered design pattern with clear separation of concerns and well-defined interfaces between components.
┌─────────────────────────────────────────────────────────────┐
│ User Interface Layer │
├─────────────────────────────────────────────────────────────┤
│ Application Layer │
├─────────────────────────────────────────────────────────────┤
│ Core Processing Layer │
├─────────────────────────────────────────────────────────────┤
│ Audio Processing Layer │
├─────────────────────────────────────────────────────────────┤
│ Hardware Abstraction Layer │
└─────────────────────────────────────────────────────────────┘
- Purpose: Central orchestration and component coordination
-
Responsibilities:
- Component lifecycle management
- Data flow coordination
- System state management
- Error handling and recovery
- Purpose: Generate controlled acoustic signals
-
Signal Types:
- Sine waves (single frequency)
- Frequency sweeps (20Hz - 20kHz)
- Pulse bursts
- Harmonic stacks
- Implementation: Mock and real audio support
- Purpose: Capture environmental acoustic responses
-
Features:
- High-fidelity audio capture
- Real-time processing
- Noise filtering
- Multi-device support
- Purpose: Extract meaningful features from audio signals
-
Techniques:
- FFT (Fast Fourier Transform)
- Spectrogram analysis
- Envelope detection
- Resonance peak extraction
- Harmonic analysis
- Purpose: Intelligent pattern recognition and interpretation
-
Approaches:
- Rule-based analysis
- Machine learning pattern matching
- Anomaly detection
- Confidence scoring
- Purpose: Continuous learning and adaptation
-
Features:
- User feedback integration
- Pattern adaptation
- Learning insights
- Performance tracking
- Purpose: Natural language user interaction
-
Capabilities:
- Command processing
- Natural language queries
- Real-time responses
- Context awareness
1. Signal Generation → 2. Audio Capture → 3. Signal Processing → 4. AI Interpretation → 5. User Interface
↓ ↓ ↓ ↓ ↓
Emission Engine Listening Module FFT Analyzer AI Interpreter Chat Interface
↓ ↓ ↓ ↓ ↓
Audio Output Audio Input Features Insights User Response
System Controller
├── Emission Engine (Audio Output)
├── Listening Module (Audio Input)
├── Signal Processing (Feature Extraction)
├── AI Interpreter (Pattern Recognition)
├── Feedback Loop (Learning System)
└── Chat Interface (User Interaction)
- Python 3.13: Primary development language
- AsyncIO: Asynchronous programming model
- NumPy/SciPy: Numerical computing and signal processing
- LibROSA: Audio analysis and feature extraction
- PyAudio: Real-time audio I/O (with mock fallback)
- SoundFile: Audio file handling
- SciPy Signal: Advanced signal processing
- Scikit-learn: Pattern recognition and classification
- LibROSA: Audio feature extraction
- Custom algorithms: Resonance-specific analysis
- FastAPI: REST API framework
- Uvicorn: ASGI server
- Pydantic: Data validation and serialization
- Prometheus Client: Metrics collection
- SQLite: Local data storage
- Custom analytics: Performance tracking
- Target latency: <1.2ms for signal processing
- Concurrent processing: AsyncIO event loop
- Memory management: Efficient buffer handling
- CPU optimization: Vectorized operations
- Modular components: Independent scaling
- Async architecture: Non-blocking operations
- Resource pooling: Efficient resource management
- Error isolation: Component-level fault tolerance
config.json (User overrides)
↓
default_config.py (System defaults)
↓
environment variables (Runtime)
↓
component configs (Component-specific)
- Audio Settings: Sample rate, channels, buffer sizes
- Processing Parameters: FFT size, window functions, thresholds
- AI Configuration: Confidence thresholds, model parameters
- System Limits: Memory usage, processing timeouts
- System Events: Start/stop, status changes
- Audio Events: Signal generation, capture completion
- Processing Events: Analysis completion, feature extraction
- User Events: Commands, queries, feedback
User Input → Command Parser → Event Dispatcher → Component Handlers → Response Generation → User Output
- Input validation: Pydantic models
- Error handling: Graceful degradation
- Resource limits: Memory and CPU constraints
- Access control: Component-level permissions
- Mock audio fallback: Prevents hardware dependency
- Error isolation: Component failure containment
- Graceful shutdown: Clean resource cleanup
- State validation: Consistency checks
- System metrics: CPU, memory, processing time
- Performance metrics: Scan duration, confidence scores
- User metrics: Interaction patterns, feedback rates
- Error metrics: Failure rates, recovery times
- Component health: Status checks and heartbeats
- System health: Overall availability and performance
- Alert thresholds: Automatic issue detection
- Performance trends: Long-term analysis
- Quantum processing: Advanced signal analysis
- Edge AI: Local processing capabilities
- Distributed processing: Multi-node scaling
- Advanced ML: Deep learning integration
- External APIs: Third-party system integration
- Cloud services: Remote processing and storage
- IoT devices: Sensor network integration
- Web interfaces: Browser-based access
Last Updated: 2026-05-06
Architecture Version: 1.0.0
Status: Production Ready