🔍 Forensic Audio Processing

Advanced audio processing tool specifically designed for forensic analysis and enhancement of quiet human vocalizations (whispers, breathing, moans, grunts) in noisy environments.

🎯 Features

• Specialized Presets: Clean Whisper, TV Suppression, Breath Detection, Vocal Isolation
• Minimal Artifacts: Optimized to reduce white noise and processing artifacts
• Real FFmpeg Processing: Professional-grade audio enhancement
• Docker Ready: Easy deployment on Vast.ai, AWS, or local machines
• Web Interface: Simple, intuitive browser-based interface
• Large File Support: Handles 10+ minute audio files efficiently (tested with 55MB files)
• Volume Boost: Up to 200% volume amplification for quiet sounds

🚀 Quick Start

One-Command Deployment (Vast.ai)

curl -sSL https://raw.githubusercontent.com/goharderr/forensic-audio-app/main/deploy_vast.sh | bash

Local Docker Setup

git clone https://github.com/goharderr/forensic-audio-app.git
cd forensic-audio-app
docker-compose up -d

Access at: http://localhost:8000

🎚️ Audio Processing Presets

✨ Clean Whisper (Recommended)

• Best for: Quiet speech, whispers with minimal artifacts
• Processing: Very gentle enhancement
• Perfect for: Initial testing and clean audio

🔧 Gentle Enhancement

• Best for: Slight audio improvement without artifacts
• Processing: Minimal noise reduction and EQ
• Perfect for: Subtle enhancement

👤 Whisper Mode

• Best for: Standard whisper enhancement
• Frequency: 30-3500 Hz
• Processing: Moderate enhancement

📺 TV Suppression

• Best for: Audio with TV background noise
• Processing: Targets TV frequencies while preserving voice
• Perfect for: Forensic scenarios with background TV

💨 Breath Detection

• Best for: Breathing, sighs, respiratory sounds
• Frequency: 100-2000 Hz
• Processing: Optimized for breath frequency range

🎵 Vocal Isolation

• Best for: Moans, grunts, vocal sounds
• Frequency: 80-8000 Hz
• Processing: Balanced vocal enhancement

📊 Performance

• Small files (< 10MB): < 30 seconds
• Medium files (10-50MB): 30-60 seconds
• Large files (50MB+): 1-3 minutes
• Memory usage: ~500MB-1GB depending on file size
• Tested with: 55MB, 10-minute audio files

🔧 System Requirements

• Docker & Docker Compose
• 2GB+ RAM (4GB recommended for large files)
• 1GB+ storage space
• Modern browser (Chrome, Firefox, Safari, Edge)

🏗️ Architecture

• Backend: FastAPI with Python 3.11
• Audio Processing: FFmpeg with custom filter chains
• Frontend: Modern HTML5 with JavaScript
• Deployment: Docker containers
• Platform: Cross-platform (Windows, Linux, macOS)

🛠️ Installation Methods

Method 1: Docker (Recommended)

git clone https://github.com/goharderr/forensic-audio-app.git
cd forensic-audio-app
docker-compose up -d

Method 2: Vast.ai Deployment

1. Rent a Vast.ai instance
2. Run the one-command deployment script
3. Access via your Vast.ai external IP

Method 3: Local Development

pip install fastapi uvicorn python-multipart
python audio_server.py

🌐 API Usage

Process Audio File

curl -X POST \
  -F "file=@input.wav" \
  -F "preset=clean_whisper" \
  http://localhost:8000/process \
  -o output.wav

Available Endpoints

• GET / - Web interface
• POST /process - Process audio file
• GET /health - Health check
• GET /debug - Debug information

📖 Use Cases

Forensic Analysis

• Whisper detection in recorded conversations
• Background noise suppression (TV, traffic, etc.)
• Breathing pattern analysis
• Voice isolation in multi-speaker recordings

Audio Enhancement

• Quiet conversation clarification
• Old recording restoration
• Interview audio cleanup
• Surveillance audio improvement

🔒 Security & Legal

⚠️ Important: This tool is designed for legitimate forensic analysis and audio enhancement purposes. Users must:

• Have proper authorization before processing any audio content
• Comply with local laws regarding audio recording and analysis
• Respect privacy and consent requirements
• Use only for legal and ethical purposes

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests if applicable
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

📄 License

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

🆘 Support

• Issues: Use GitHub Issues for bug reports
• Discussions: Use GitHub Discussions for questions
• Wiki: Check the Wiki for advanced usage

🔄 Changelog

v2.0.0 (Latest)

• ✨ Added Clean Whisper and Gentle Enhancement presets
• 🐛 Fixed white noise and artifact issues
• 🔧 Optimized FFmpeg filter chains
• 📈 Improved processing performance
• 🛡️ Enhanced error handling

v1.0.0

• 🎉 Initial release
• 🎚️ Basic preset system
• 🐳 Docker deployment support

🙏 Acknowledgments

• FFmpeg community for the excellent audio processing library
• FastAPI team for the robust web framework
• Docker for containerization technology
• Vast.ai for accessible GPU/CPU rental platform

📊 Project Stats

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Made with ❤️ for the forensic audio analysis community