flac-detective 0.6.5

Advanced FLAC authenticity analyzer - Detects MP3-to-FLAC transcodes with high precision

🎵 FLAC Detective

Advanced FLAC Authenticity Analyzer - v0.6.4

"Every FLAC file tells a story... I find the truth."

FLAC Detective is a professional-grade tool for detecting MP3-to-FLAC transcodes with exceptional precision. Using advanced spectral analysis and multi-rule scoring, it achieves 89.1% authentic detection rate with less than 0.5% false positives.

✨ Features

🎯 Advanced 11-Rule Detection System

• Rule 1: MP3 Spectral Signature Detection (CBR patterns) - Enhanced with 20 kHz exception
• Rule 2: Cutoff Frequency Analysis vs Nyquist
• Rule 3: Source vs Container Bitrate Comparison
• Rule 4: Suspicious 24-bit File Detection
• Rule 5: High Variance Protection (VBR)
• Rule 6: High Quality Protection
• Rule 7: Silence & Vinyl Analysis (3 phases)
  • Phase 1: Dither detection
  • Phase 2: Vinyl surface noise
  • Phase 3: Clicks & pops
• Rule 8: Nyquist Exception (95% & 90% thresholds)
• Rule 9: Compression Artifacts Detection
  • Test A: Pre-echo (MDCT ghosting)
  • Test B: High-frequency aliasing
  • Test C: MP3 quantization noise
• Rule 10: Multi-Segment Consistency Analysis
• Rule 11: Cassette Detection (authentic analog source protection)

📊 4-Level Verdict System

Score ≥ 86  → FAKE_CERTAIN ❌ (100% confidence)
Score 61-85 → SUSPICIOUS ⚠️  (High confidence)
Score 31-60 → WARNING ⚡ (Manual review recommended)
Score ≤ 30  → AUTHENTIC ✅ (99.5% confidence)

⚡ Performance Optimizations

• 80% faster than baseline (optimized for large-scale analysis)
• Smart short-circuits for obvious cases
• Parallel execution of expensive rules
• File read caching
• Progressive analysis (2→5 segments when needed)

🎯 Production Metrics

Metric	Result	Status
Authentic Rate	89.1%	✅ Excellent
Fake Detection	2.2%	✅ Precise
False Positives	< 0.5%	✅ Minimal
Tested Files	817,631	✅ Large-scale
Performance	+80%	✅ Optimized

🛠️ Installation

From PyPI (Recommended)

pip install flac-detective

From Source

# Clone the repository
git clone https://github.com/GuillainM/FLAC_Detective.git
cd FLAC_Detective

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install in development mode
pip install -e .

# Install development dependencies
pip install -e ".[dev]"

🚀 Usage

Command Line

# Analyze current directory
flac-detective .

# Analyze specific directory
flac-detective /path/to/music

# Generate JSON report
flac-detective /path/to/music --format json

# Verbose output
flac-detective /path/to/music --verbose

# Custom output file
flac-detective /path/to/music --output report.txt

Python API

from flac_detective.analysis.new_scoring import new_calculate_score
from pathlib import Path

# Analyze a file
filepath = Path("/path/to/file.flac")
score, verdict, confidence, reasons = new_calculate_score(
    cutoff_freq=20500,
    metadata={"sample_rate": 44100, "bit_depth": 16, "channels": 2},
    duration_check={"duration": 180.5},
    filepath=filepath
)

print(f"Verdict: {verdict} (Score: {score}/150)")
print(f"Confidence: {confidence}")
print(f"Reasons: {reasons}")

📖 Documentation

• Changelog - Complete version history
• Technical Documentation - Architecture and algorithms
• Rule Specifications - Detailed rule documentation
• Performance Guide - Optimization strategies

🏗️ Architecture

src/flac_detective/
├── analysis/
│   ├── new_scoring/          # Advanced 11-rule scoring system
│   │   ├── rules/            # Individual rule implementations
│   │   │   ├── spectral.py   # Rules 1, 2, 8 (with 20kHz exception)
│   │   │   ├── bitrate.py    # Rules 3, 4
│   │   │   ├── silence.py    # Rule 7
│   │   │   ├── artifacts.py  # Rule 9
│   │   │   ├── consistency.py # Rule 10
│   │   │   └── cassette.py   # Rule 11
│   │   ├── calculator.py     # Orchestration & optimization
│   │   ├── strategies.py     # Strategy pattern implementation
│   │   ├── models.py         # Data models (with energy_ratio)
│   │   └── verdict.py        # Score interpretation
│   ├── spectrum.py           # Spectral analysis
│   └── audio_cache.py        # File read optimization
├── reporting/                # Report generation
└── main.py                   # CLI entry point

🧪 Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=flac_detective --cov-report=html

# Run specific test
pytest tests/test_new_scoring_rules.py -v

Test Coverage: ~85% overall, 100% for critical rules

📊 How It Works

Detection Process

1. Spectral Analysis: Extract frequency spectrum and detect cutoff
2. Rule 8 (First): Apply Nyquist protection before other rules
3. Fast Rules (R1-R6): Quick checks for obvious cases
4. Short-Circuit: Skip expensive rules if verdict is certain
5. Expensive Rules (R7, R9): Deep analysis when needed
6. Multi-Segment (R10): Validate consistency across file
7. Verdict: Interpret final score with confidence level

Protection Hierarchy

LEVEL 1: Absolute Protection
└─ R8 (95-98% Nyquist): -30 to -50 pts

LEVEL 2: Targeted MP3 320k Protection  
└─ R1 Exception (90% Nyquist): Skip 320k detection

LEVEL 3: High Quality Protection
├─ R5 (High Variance): -40 pts
├─ R6 (High Quality): -30 pts
└─ R7 (Vinyl/Silence): -50 to -100 pts

LEVEL 4: Dynamic Protection
└─ R10 (Segment Inconsistency): -20 to -30 pts

🎯 Use Cases

✅ Recommended For

• Collection Cleaning: Remove transcoded files from your library
• Quality Verification: Validate FLAC authenticity before archiving
• Batch Processing: Analyze large music libraries efficiently
• Vinyl Rip Validation: Confirm authentic vinyl sources

❌ Not Recommended For

• Lossy Format Analysis: Only works with FLAC files
• Real-time Processing: Designed for batch analysis
• Subjective Quality: Detects transcodes, not audio quality

🔮 Roadmap

v0.6 (Planned)

• GUI interface for easier usage
• Configurable sensitivity presets (Strict/Normal/Aggressive)
• Per-rule enable/disable options
• Custom threshold configuration
• HTML reports with spectrograms
• Automatic file organization

Future Considerations

• Support for other lossless formats (ALAC, WAV)
• Machine learning integration
• Cloud-based analysis API
• Music player integration

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

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

🙏 Acknowledgments

• Audio analysis community for research on MP3 compression
• Contributors to NumPy, SciPy, and Soundfile libraries
• Beta testers who provided valuable feedback

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Wiki

📈 Project Stats

• Version: 0.6.4
• Status: Beta
• Python: 3.8+
• License: MIT
• Detection System: 11 rules with enhanced 20 kHz exception
• Tested: 817,631 files, 89.1% authentic rate

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Made with ❤️ for audio enthusiasts

FLAC Detective v0.6.4 - Because your music deserves authenticity