HarmonyDagger

HarmonyDagger is a tool for audio protection against generative AI models, introducing imperceptible psychoacoustic noise patterns that prevent effective machine learning while preserving human listening quality.

Features

• Psychoacoustic Masking: Uses principles of human auditory perception to generate strategic noise
• Adaptive Scaling: Adjusts protection strength based on signal characteristics
• Phase Perturbation: Subtle phase shifts that disrupt AI feature extraction while remaining imperceptible
• Temporal Forward Masking: Exploits post-masking effects to hide more aggressive perturbations after loud events
• Vocal-Specific Mode: Optimized protection for the human vocal range (300Hz-3kHz) targeting AI voice cloning
• Dry/Wet Control: Balance protection strength vs. audio fidelity with a single parameter
• Multi-channel Support: Works with both mono and stereo audio files
• Multiple Audio Format Support: Processes and outputs WAV, MP3, FLAC, and OGG files
  • MP3 support requires ffmpeg to be installed on your system
  • FLAC and OGG support is built-in
• Robustness Testing: Verify that perturbations survive MP3 compression, low-pass filtering, and resampling
• Protection Verification: MFCC and spectral analysis to measure protection effectiveness
• Benchmark Reporting: SNR and perturbation metrics for transparency
• Visualization Tools: Optional visual analytics of audio perturbations
• Parallel Batch Processing: Process multiple files efficiently using multiple CPU cores
• Streamlit Web Demo: Upload audio and hear protected version in the browser
• Docker Support: One-command deployment with Docker Compose
• PyPI Package: Easy installation via pip

Installation

From PyPI

pip install harmonydagger

From Source

git clone https://github.com/jaschadub/harmonydagger.git
cd harmonydagger
pip install -e .

With Streamlit Demo

pip install -e ".[streamlit]"
streamlit run streamlit_app.py

With Docker

docker compose up --build
# Open http://localhost:8501

Usage

Command Line Interface

# Basic protection
harmonydagger input.wav -o output.wav -n 0.1 -a

# Full protection with all techniques
harmonydagger input.wav -o output.wav -n 0.1 -a --phase --temporal-masking --vocal-mode

# Adjust protection strength (0.0 = original, 1.0 = full protection)
harmonydagger input.wav -o output.wav -n 0.1 -a -d 0.7

# Process with robustness check and verification
harmonydagger input.wav -o output.wav -n 0.1 -a --robust --verify --benchmark -v

# Process multiple files in parallel
harmonydagger input_directory -o output_directory -j 4

# Process only MP3 files in a directory
harmonydagger input_directory -o output_directory -f mp3

# Get help on all available options
harmonydagger --help

Python API

import librosa
from harmonydagger.core import generate_protected_audio

# Load audio file
audio, sr = librosa.load('input.wav', sr=None)

# Apply full protection with all techniques
protected_audio = generate_protected_audio(
    audio, sr,
    window_size=2048,
    hop_size=512,
    noise_scale=0.1,
    adaptive_scaling=True,
    dry_wet=1.0,
    vocal_mode=True,
    use_phase_perturbation=True,
    use_temporal_masking=True,
)

# Save result
import soundfile as sf
sf.write('output.wav', protected_audio, sr)

# Verify protection effectiveness
from harmonydagger.verify import verify_protection
report = verify_protection(audio, protected_audio, sr)
print(f"Protection score: {report['protection_score']:.3f}")

# Test robustness against common transforms
from harmonydagger.robustness import augment_and_check_survival
perturbation = protected_audio - audio
survival = augment_and_check_survival(audio, perturbation, sr)
for transform, ratio in survival.items():
    print(f"  {transform}: {ratio:.1%} survival")

Batch Processing with Parallelization

from harmonydagger.file_operations import parallel_batch_process, recursive_find_audio_files

# Find all audio files in a directory (supports MP3, FLAC, OGG, and WAV)
audio_files = recursive_find_audio_files('./audio_files')

# Process files in parallel with new features
results = parallel_batch_process(
    audio_files,
    output_dir='./protected_audio',
    window_size=2048,
    hop_size=512,
    noise_scale=0.1,
    adaptive_scaling=True,
    max_workers=4,
    vocal_mode=True,
    use_phase_perturbation=True,
    use_temporal_masking=True,
)

for file_path, result in results.items():
    if result['success']:
        print(f"Successfully processed {file_path} in {result['processing_time']:.2f} seconds")
    else:
        print(f"Failed to process {file_path}: {result['error']}")

Command Line Options

usage: harmonydagger [-h] [-o OUTPUT] [-w WINDOW_SIZE] [-s HOP_SIZE]
                     [-n NOISE_SCALE] [-a] [-d DRY_WET] [--vocal-mode]
                     [--phase] [--temporal-masking] [--robust] [--verify]
                     [--benchmark] [-m] [-j JOBS] [-v]
                     [-f {wav,mp3,flac,ogg,all}]
                     [--visualize] [--visualize_diff] [--version]
                     input

positional arguments:
  input                 Input audio file or directory containing audio files

options:
  -h, --help            show this help message and exit
  -o OUTPUT, --output OUTPUT
                        Output file or directory (default: input_protected.wav)
  -w WINDOW_SIZE, --window-size WINDOW_SIZE
                        STFT window size (default: 2048)
  -s HOP_SIZE, --hop-size HOP_SIZE
                        STFT hop size (default: 512)
  -n NOISE_SCALE, --noise-scale NOISE_SCALE
                        Noise scale (0-1) (default: 0.1)
  -a, --adaptive-scaling
                        Use adaptive noise scaling based on signal strength
  -d DRY_WET, --dry-wet DRY_WET
                        Dry/wet mix (0.0=original, 1.0=fully protected) (default: 1.0)
  --vocal-mode          Optimize protection for vocal frequencies (300Hz-3kHz)
  --phase               Add phase perturbation (disrupts AI feature extraction)
  --temporal-masking    Add temporal forward masking noise
  --robust              Test perturbation robustness against common transforms
  --verify              Run protection verification after processing
  --benchmark           Show SNR and perturbation metrics after processing
  -m, --force-mono      Convert stereo to mono before processing
  -j JOBS, --jobs JOBS  Number of parallel processing jobs (for batch processing) (default: 1)
  -v, --verbose         Enable verbose output
  -f {wav,mp3,flac,ogg,all}, --format {wav,mp3,flac,ogg,all}
                        Specify audio format to process (when processing directories) (default: all)

Visualization:
  --visualize           Show spectrogram comparison of original and perturbed audio
  --visualize_diff      Visualize the difference between original and perturbed audio

  --version             show program's version number and exit

How It Works

HarmonyDagger works by analyzing the audio in the frequency domain using Short-Time Fourier Transform (STFT), then applying carefully calibrated noise based on psychoacoustic principles:

1. Frequency Analysis: Converts audio to time-frequency representation
2. Psychoacoustic Modeling: Identifies perceptual masking thresholds
3. Strategic Perturbation: Adds noise patterns imperceptible to humans
4. Phase Perturbation: Subtle phase shifts that disrupt AI feature extraction
5. Temporal Masking: Hides perturbations in the temporal shadow of loud events
6. Adaptive Scaling: Adjusts protection based on signal characteristics

Benchmarks

Protection quality at different noise scale settings (measured on 440Hz sine wave, sr=22050):

Setting	SNR (dB)	Perturbation Ratio	Description
noise_scale=0.01	~45 dB	~0.005	Minimal protection
noise_scale=0.05	~32 dB	~0.025	Light protection
noise_scale=0.10	~26 dB	~0.050	Recommended default
noise_scale=0.20	~20 dB	~0.100	Strong protection

Use --benchmark flag to see exact metrics for your audio files.

ffmpeg Compatibility

ffmpeg is required for MP3 input/output and MP3 robustness testing.

• Minimum version: ffmpeg 4.0+
• Recommended: ffmpeg 5.x or 6.x

Install ffmpeg

# Ubuntu/Debian
sudo apt-get install ffmpeg

# macOS
brew install ffmpeg

# Windows
choco install ffmpeg

Docker

Quick Start

docker compose up --build

The Streamlit demo will be available at http://localhost:8501.

CLI in Docker

docker build -t harmonydagger .
docker run -v $(pwd)/audio:/data harmonydagger harmonydagger /data/input.wav -o /data/output.wav -n 0.1 -a

Contributing

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

License

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

Citation

If you use HarmonyDagger in your research, please cite:

@misc{harmonydagger2025,
  author = {HarmonyDagger Team},
  title = {HarmonyDagger: Making Audio Content Unlearnable for AI},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/jaschadub/harmonydagger}
}