SCRIBE FAQ and Best Practices

SCRIBE Resonance AI System - Documentation

Documentation
Technical Reference

SCRIBE FAQ and Best Practices

❓ Frequently Asked Questions

General Questions

Q: What is SCRIBE?

A: SCRIBE (Sonic Resonance Intelligence and Behavioral Exploration) is an advanced resonance intelligence platform that uses acoustic signals to analyze and interpret environments, materials, and structural properties through active sensing and AI-powered pattern recognition.

Q: How does SCRIBE work?

A: SCRIBE emits controlled acoustic signals, captures the environmental response, processes the signals using FFT and other techniques, interprets the patterns using AI, and provides insights about materials, environments, and structural properties.

Q: What can SCRIBE detect?

A: SCRIBE can detect:

  • Material types (wood, metal, plastic, glass, etc.)
  • Environmental characteristics (room size, reverberation, etc.)
  • Structural properties (resonances, anomalies, etc.)
  • Acoustic properties (frequency response, harmonics, etc.)

Q: How accurate is SCRIBE?

A: Accuracy varies by environment and conditions, but typically ranges from 70-90% confidence for well-controlled environments. The system learns and improves over time with user feedback.

Installation and Setup

Q: What are the system requirements?

A: Minimum requirements:

  • Python 3.13+
  • 2GB RAM
  • 1GB disk space
  • Audio hardware (optional, mock audio available)

Q: Do I need special audio hardware?

A: No. SCRIBE includes a mock audio system for development and testing. For production use, real audio hardware is recommended but not required.

Q: How do I install SCRIBE?

A: Run the deployment script:

./deploy.sh

This will set up the virtual environment, install dependencies, and validate the installation.

Q: Can I run SCRIBE on Windows/Mac/Linux?

A: Yes, SCRIBE is cross-platform compatible. The installation script works on all major operating systems.

Usage and Operation

Q: How do I start using SCRIBE?

A: Use the interactive mode:

./start_interactive.sh

Then type commands like /scan or ask natural language questions.

Q: What commands are available?

A: Common commands:

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

Q: Can I use SCRIBE programmatically?

A: Yes. Start the API server:

./start_api.sh

Then use the REST API endpoints documented in the API section.

Q: How do I improve accuracy?

A: Provide feedback on scan results:

 SCRIBE> /feedback material oak
 SCRIBE> /feedback rating 5

The system learns from corrections and improves over time.

Technical Questions

Q: What audio formats does SCRIBE support?

A: SCRIBE supports:

  • Sample rates: 22050, 44100, 48000, 96000 Hz
  • Channels: Mono (1) or Stereo (2)
  • Formats: 16-bit, 32-bit, and 32-bit float

Q: What signal types can SCRIBE generate?

A: SCRIBE can generate:

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

Q: How long does a scan take?

A: Typical scan duration:

  • Signal generation: 0.1-0.5 seconds
  • Audio capture: 1-5 seconds (configurable)
  • Processing: 0.5-2 seconds
  • Total: 2-8 seconds

Q: Can SCRIBE run in real-time?

A: Yes, SCRIBE can perform real-time analysis with appropriate configuration. Use the performance optimization settings for real-time operation.

Troubleshooting

Q: SCRIBE says "No module named 'pyaudio'"

A: PyAudio is optional. SCRIBE will automatically use mock audio if PyAudio is not available. For real audio, install PyAudio:

pip install pyaudio

Q: Scans have low confidence scores

A: Common causes and solutions:

  • Background noise: Minimize environmental noise
  • Poor positioning: Keep microphone/sensor consistent
  • Wrong parameters: Adjust frequency and duration for your target
  • Hardware issues: Check audio device connections

Q: API server won't start

A: Check for:

  • Port conflicts: lsof -i :8000
  • Missing dependencies: pip install fastapi uvicorn
  • Permission issues: Use appropriate user permissions

Q: Memory usage is high

A: Solutions:

  • Reduce scan history: Set max_history in config
  • Use smaller buffers: Reduce chunk_size and buffer_size
  • Restart system: Clear accumulated memory

Integration Questions

Q: Can I integrate SCRIBE with my existing system?

A: Yes. SCRIBE provides:

  • REST API for HTTP integration
  • WebSocket for real-time communication
  • Database access for direct data integration
  • Message queue support for asynchronous processing

Q: How do I authenticate API calls?

A: Use API key authentication:

client = ScribeClient(api_key="your-api-key")

Configure API keys in the security settings.

Q: Can SCRIBE run in Docker?

A: Yes. A Dockerfile is provided:

docker build -t scribe:latest .
docker run -p 8000:8000 scribe:latest

Q: How do I monitor SCRIBE performance?

A: Use the built-in monitoring:

  • Prometheus metrics on port 8001
  • Performance tracking in logs
  • Health check endpoint: /health

Licensing and Commercial Use

Q: Is SCRIBE open source?

A: Yes, SCRIBE is released under the MIT license. See the LICENSE file for details.

Q: Can I use SCRIBE commercially?

A: Yes, the MIT license permits commercial use. Please retain the copyright notice.

Q: Are there any usage restrictions?

A: No, there are no usage restrictions. However, please ensure compliance with local regulations regarding acoustic emissions.

Q: Can I redistribute SCRIBE?

A: Yes, you can redistribute SCRIBE under the same MIT license terms.

Best Practices

System Setup

✅ DO: Use Virtual Environment

python3 -m venv scribe_env
source scribe_env/bin/activate

❌ DON'T: Install globally

Global installation can cause conflicts with other Python packages.

✅ DO: Validate Installation

python3 validate_system.py

❌ DON'T: Skip validation

Always validate the installation before use.

Configuration

✅ DO: Tune for Your Use Case

{
  "audio": {
    "sample_rate": 44100,
    "use_real_audio": true
  },
  "processing": {
    "window_size": 2048
  }
}

❌ DON'T: Use default settings for all scenarios

Different use cases require different configurations.

✅ DO: Monitor Performance

# Check system resources
top
htop

❌ DON'T: Ignore performance issues

Address performance problems early.

Scan Operations

✅ DO: Use Appropriate Parameters

# Material analysis
 SCRIBE> /scan --type=sine --frequency=440 --duration=2

# Room analysis
 SCRIBE> /scan --type=sweep --duration=5

❌ DON'T: Use one-size-fits-all parameters

Different targets require different parameters.

✅ DO: Provide Consistent Feedback

 SCRIBE> /feedback material oak
 SCRIBE> /feedback rating 5

❌ DON'T: Skip feedback

Feedback improves system accuracy.

✅ DO: Minimize Background Noise

  • Close windows and doors
  • Turn off noisy equipment
  • Use consistent positioning

❌ DON'T: Scan in noisy environments

Background noise reduces accuracy.

API Usage

✅ DO: Implement Error Handling

try:
    result = client.perform_scan(config)
except requests.RequestException as e:
    print(f"API error: {e}")

❌ DON'T: Assume API calls always succeed

Network issues can cause failures.

✅ DO: Use Rate Limiting

import time

def scan_with_delay(config):
    result = client.perform_scan(config)
    time.sleep(1)  # Rate limiting
    return result

❌ DON'T: Overload the API

Excessive requests can cause performance issues.

✅ DO: Cache Results When Appropriate

from functools import lru_cache

@lru_cache(maxsize=100)
def cached_scan(frequency, duration):
    config = ScanConfig(frequency=frequency, duration=duration)
    return client.perform_scan(config)

❌ DON'T: Cache everything

Some data should always be fresh.

Production Deployment

✅ DO: Use HTTPS

server {
    listen 443 ssl;
    ssl_certificate /path/to/cert.pem;
    ssl_certificate_key /path/to/key.pem;
}

❌ DON'T: Use HTTP in production

Unencrypted traffic is insecure.

✅ DO: Implement Monitoring

# Health check endpoint
@app.get("/health")
async def health():
    return {"status": "healthy"}

❌ DON'T: Deploy without monitoring

You need to know when problems occur.

✅ DO: Use Environment Variables

export SCRIBE_API_KEY="your-secret-key"
export SCRIBE_DB_PATH="/data/scribe.db"

❌ DON'T: Hardcode credentials

Credentials should never be in source code.

Development

✅ DO: Follow Coding Standards

# Use type hints
def perform_scan(config: ScanConfig) -> Dict[str, Any]:
    """Perform a resonance scan."""
    pass

# Use docstrings
class ScribeClient:
    """Client for SCRIBE API."""
    pass

❌ DON'T: Ignore code quality

Maintainable code is important.

✅ DO: Write Tests

import pytest

def test_scan_performance():
    client = ScribeClient()
    config = ScanConfig(frequency=440, duration=1.0)
    result = client.perform_scan(config)
    assert 'interpretation' in result

❌ DON'T: Skip testing

Tests prevent regressions.

✅ DO: Document Changes

## Changes
- Added new signal type 'chirp'
- Improved error handling
- Updated documentation

❌ DON'T: Make undocumented changes

Documentation helps other developers.

Security

✅ DO: Use API Keys

client = ScribeClient(api_key="secure-api-key")

❌ DON'T: Expose API without authentication

Unprotected APIs are security risks.

✅ DO: Validate Input

def validate_frequency(frequency):
    if not 20 <= frequency <= 20000:
        raise ValueError("Frequency out of range")

❌ DON'T: Trust user input

Always validate external data.

✅ DO: Use HTTPS

# Use HTTPS URLs
client = ScribeClient(base_url="https://scribe.example.com")

❌ DON'T: Use HTTP for sensitive data

HTTP traffic can be intercepted.

Data Management

✅ DO: Backup Regularly

# Backup database
cp scribe_learning.db backup/scribe_$(date +%Y%m%d).db

❌ DON'T: Lose data

Regular backups prevent data loss.

✅ DO: Clean Old Data

# Clean old scan history
if len(scan_history) > max_history:
    scan_history = scan_history[-max_history:]

❌ DON'T: Let data grow indefinitely

Unlimited growth causes performance issues.

✅ DO: Monitor Storage

# Check disk usage
df -h
du -sh scribe_learning.db

❌ DON'T: Ignore storage limits

Running out of space causes failures.

Advanced Tips

Performance Optimization

Use Appropriate Buffer Sizes

{
  "audio": {
    "chunk_size": 1024,
    "buffer_size": 4096
  }
}

Optimize FFT Parameters

{
  "processing": {
    "window_size": 2048,
    "n_fft": 2048
  }
}

Enable Caching

from functools import lru_cache

@lru_cache(maxsize=1000)
def cached_interpretation(features_hash):
    return interpret_features(features_hash)

Accuracy Improvement

Use Multiple Frequencies

frequencies = [220, 440, 880, 1760]
results = []
for freq in frequencies:
    result = client.perform_scan(ScanConfig(frequency=freq))
    results.append(result)

Provide Consistent Feedback

# Always provide feedback
for scan in scans:
    if scan.confidence < 0.7:
        client.add_feedback(scan.scan_id, "rating", 3)

Train for Specific Environments

# Specialize for room acoustics
config = {
    "ai": {
        "confidence_threshold": 0.8,
        "pattern_adaptation": True
    }
}

Integration Patterns

Circuit Breaker Pattern

class CircuitBreaker:
    def __init__(self, failure_threshold=5):
        self.failure_threshold = failure_threshold
        self.failure_count = 0
    
    def call(self, func, *args, **kwargs):
        if self.failure_count >= self.failure_threshold:
            raise Exception("Circuit breaker open")
        
        try:
            return func(*args, **kwargs)
        except:
            self.failure_count += 1
            raise

Retry Pattern

import time

def retry_call(func, max_attempts=3):
    for attempt in range(max_attempts):
        try:
            return func()
        except Exception as e:
            if attempt == max_attempts - 1:
                raise
            time.sleep(2 ** attempt)

Common Scenarios

Material Identification

# Best practices for material analysis
 SCRIBE> /scan --type=sine --frequency=440 --duration=2
 SCRIBE> /scan --type=sine --frequency=880 --duration=2
 SCRIBE> Compare these scans
 SCRIBE> /feedback material oak

Room Acoustics

# Best practices for room analysis
 SCRIBE> /scan --type=sweep --duration=5
 SCRIBE> How reverberant is this room?
 SCRIBE> What are the room modes?

Quality Control

# Best practices for quality control
 SCRIBE> /scan --type=pulse --duration=1
 SCRIBE> Detect any anomalies
 SCRIBE> /feedback rating 5

Continuous Monitoring

# Best practices for monitoring
import asyncio

async def monitor_continuously():
    client = ScribeClient()
    
    while True:
        result = client.perform_scan(ScanConfig(frequency=440, duration=2))
        
        if result['interpretation']['confidence_scores']['overall'] < 0.7:
            # Send alert
            await send_alert(result)
        
        await asyncio.sleep(60)  # Check every minute

Troubleshooting Checklist

Before Contacting Support

  • [ ] Check system status: /status
  • [ ] Run validation: python3 validate_system.py
  • [ ] Check logs: tail -f scribe.log
  • [ ] Verify configuration: Review config.json
  • [ ] Test with simple scan: /scan --type=sine --frequency=440
  • [ ] Check system resources: top, df -h

Common Issues and Solutions

| Issue | Solution | |-------|----------| | Low confidence | Reduce noise, adjust parameters, provide feedback | | Slow performance | Optimize configuration, check resources | | API errors | Check connectivity, verify API key | | Audio issues | Use mock audio, check hardware | | Memory issues | Reduce history, restart system |

Getting Help

  1. Check this FAQ - Most issues are covered here
  2. Review documentation - Check relevant sections
  3. Search issues - Look for similar problems
  4. Provide details - Include error messages and configuration
  5. Describe environment - OS, Python version, hardware specs

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