ai-audio-detector 1.1.0

Machine learning system for detecting AI-generated audio using Benford's Law and advanced spectral features

AI Audio Detector

A machine learning system for detecting AI-generated audio using Benford's Law analysis and advanced audio feature extraction. The system employs ensemble learning with adaptive model updating capabilities.

Features

• Multi-Model Ensemble: Uses Random Forest, Gradient Boosting, SGD, and Passive Aggressive classifiers
• Benford's Law Analysis: Analyzes frequency distributions for AI detection patterns
• Comprehensive Audio Features: Extracts spectral, temporal, and compression-related features
• Adaptive Learning: Supports incremental model updates with new data
• Batch Processing: Parallel processing for large audio datasets
• Spectrogram Generation: Creates and compares various types of spectrograms
• Interactive CLI: User-friendly command-line interface

Supported Audio Formats

• WAV (.wav)
• MP3 (.mp3)
• FLAC (.flac)
• OGG (.ogg)
• M4A (.m4a)
• AAC (.aac)

Installation

Option 1: Install from PyPI (Recommended)

pip install ai-audio-detector

Option 2: Install from Source

1. Clone the repository:

git clone https://github.com/ajprice16/AI_Audio_Detection.git
cd AI_Audio_Detection

2. Install dependencies:

pip install -r requirements.txt

System Dependencies

On Ubuntu/Debian:

sudo apt-get install libsndfile1 ffmpeg

On macOS:

brew install libsndfile ffmpeg

Quick Start

Training Initial Models

1. Prepare your data: Organize your audio files into two directories:

   • human_audio/ - Human-generated audio files
   • ai_audio/ - AI-generated audio files

2. Run the detector:

If installed from PyPI:

ai-audio-detector --interactive
# or
ai-audio-detector --predict-file path/to/audio.wav

If running from source:

python -m ai_audio_detector --interactive
# or
python -m ai_audio_detector --predict-file path/to/audio.wav

3. Choose option 1 to train new models and follow the prompts.

Command Line Usage

Train models:

ai-audio-detector --train --human-dir path/to/human/audio --ai-dir path/to/ai/audio

Predict single file:

ai-audio-detector --predict-file path/to/audio.wav

Predict batch:

ai-audio-detector --predict-batch path/to/audio/directory

Interactive mode:

ai-audio-detector --interactive

Predicting Single Files

Interactive mode:

ai-audio-detector --interactive
# Choose option 2 and enter the path to your audio file

Direct command:

ai-audio-detector --predict-file path/to/audio.wav

Batch Prediction

Interactive mode:

ai-audio-detector --interactive
# Choose option 3 and enter the directory path

Direct command:

ai-audio-detector --predict-batch path/to/audio/directory

Advanced Usage

Programmatic Usage

from ai_audio_detector import AIAudioDetector
from pathlib import Path

# Initialize detector
detector = AIAudioDetector(base_dir=Path.cwd())

# Train models
human_features = detector.extract_features_from_directory("human_audio/", is_ai_directory=False)
ai_features = detector.extract_features_from_directory("ai_audio/", is_ai_directory=True)

all_features = human_features + ai_features
df_results = pd.DataFrame(all_features)
training_results = detector.train_models(df_results)

# Make predictions
result = detector.predict_file("test_audio.wav")
print(f"Prediction: {'AI' if result['is_ai'] else 'Human'}")
print(f"Confidence: {result['confidence']:.3f}")

Adaptive Learning

The system supports adaptive learning to improve accuracy with new data:

# Add new AI data
detector.add_ai_data("new_ai_audio/", retrain_batch_models=True)

# Add new human data
detector.add_human_data("new_human_audio/", retrain_batch_models=True)

# Add mixed data batch
directories = [
    {'path': 'dataset1/', 'is_ai': True},
    {'path': 'dataset2/', 'is_ai': False}
]
detector.add_mixed_data_batch(directories, retrain_batch_models=True)

Features Extracted

Benford's Law Features

• Chi-square test statistics
• Kolmogorov-Smirnov test statistics
• Mean absolute deviation from expected distribution
• Maximum deviation
• Entropy measures

Spectral Features

• Spectral centroid, bandwidth, rolloff
• MFCCs (13 coefficients + standard deviations)
• Chroma features
• Spectral contrast
• Zero crossing rate

Temporal Features

• RMS energy (mean and standard deviation)
• Tempo estimation
• Spectral flatness
• Dynamic range
• Peak-to-RMS ratio

Compression Features

• Estimated bit depth
• Clipping detection
• DC offset
• High frequency content ratio

Model Architecture

The system uses an ensemble of four different models:

1. Incremental Models (for adaptive learning):

   • SGD Classifier with log loss
   • Passive Aggressive Classifier

2. Batch Models (for maximum accuracy):

   • Random Forest (200 estimators)
   • Gradient Boosting (200 estimators)

All features are standardized using StandardScaler, and final predictions use ensemble averaging.

Configuration

Modify config.yaml to customize:

• Model parameters
• Feature extraction settings
• Processing options
• Output directories

Command Line Options

1. Train new models - Initial training from audio directories
2. Predict single file - Analyze one audio file
3. Predict batch - Analyze all files in a directory
4. Update models - Adaptive learning with new data
5. Add AI data - Add new AI samples to existing models
6. Add Human data - Add new human samples to existing models
7. Batch directories - Add multiple directories at once
8. Training history - View model training history
9. Data balance - Check AI vs Human data balance
10. Create visualizations - Generate analysis plots
11. Generate spectrograms - Create spectrograms for audio files
12. Spectrogram comparison - Compare AI vs Human spectrograms

Output Files

• models/ai_audio_detector.joblib - Trained models and metadata
• training_results.csv - Detailed training data and features
• ai_detection_analysis.png - Visualization plots
• spectrograms/ - Generated spectrogram images
• spectrogram_comparisons/ - Side-by-side comparisons

Performance Considerations

• Multiprocessing: Automatically used for batches > 3 files
• Memory Management: Spectrograms are generated efficiently with proper cleanup
• Scalability: Incremental learning allows handling large datasets over time

Requirements

• Python 3.7+
• librosa (audio processing)
• scikit-learn (machine learning)
• pandas, numpy (data manipulation)
• matplotlib (visualization)
• scipy (statistical tests)

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

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

Acknowledgments

• Uses Benford's Law for detecting artificial patterns in audio
• Built on librosa for robust audio feature extraction
• Employs scikit-learn for machine learning capabilities

Citation

If you use this work in your research, please cite:

@software{ai_audio_detector,
  title={AI Audio Detector: Machine Learning System for Detecting AI-Generated Audio},
  author={Alex Price},
  year={2025},
  url={https://github.com/yourusername/ai-audio-detector}
}