sysex-toolkit 1.0.0

Universal SysEx decoder/encoder library for synthesizers

SysEx Toolkit

Universal SysEx decoder/encoder library for synthesizers and music production.

🎹 Features

• Universal SysEx Support: Works with any synthesizer through configuration files
• Built-in Definitions: Access Virus C, Roland JP-8000, and more
• ML Ready: Normalized parameters for machine learning applications
• Easy Integration: Simple API for quick adoption
• Extensible: Add new synthesizers via JSON/YAML configs

🚀 Quick Start

Installation

pip install sysex-toolkit

Basic Usage

from sysex_toolkit import decode_sysex_file, SysExFormat

# Decode a preset
presets = decode_sysex_file('my_preset.syx', SysExFormat.ACCESS_VIRUS)

# Access parameters
for preset in presets:
    print(f"Preset: {preset['metadata']['preset_name']}")
    
    # Filter parameters (great for wobble bass analysis)
    filter_params = {
        name: data for name, data in preset['parameters'].items() 
        if data['category'] == 'filter'
    }
    
    print(f"Filter cutoff: {filter_params['filter_cutoff']['normalized_value']:.3f}")

Advanced Usage

from sysex_toolkit import SysExLibrary, SysExFormat

# Create library
library = SysExLibrary()

# Get decoder for specific synthesizer
decoder = library.get_decoder(SysExFormat.ACCESS_VIRUS)

# Decode multiple presets
presets = decoder.decode_file('factory_presets.syx')

# Analyze for machine learning
ml_features = []
for preset in presets:
    # Extract normalized values for ML
    feature_vector = [
        param['normalized_value'] 
        for param in preset['parameters'].values()
    ]
    ml_features.append(feature_vector)

🎛️ Supported Synthesizers

• Access Virus C (Full support)
• Roland JP-8000 (Basic support)
• Custom synthesizers via configuration files

🔧 CLI Tools

# Decode a SysEx file
sysex-decode my_preset.syx --synth access_virus

# Analyze unknown SysEx format
sysex-analyze unknown_synth.syx

# Batch process multiple files
sysex-batch /path/to/presets/ --synth access_virus

📊 Machine Learning Integration

Perfect for electronic music ML research:

# Extract features for latent space
import numpy as np
from sysex_toolkit import decode_sysex_file

presets = decode_sysex_file('dubstep_presets.syx')

# Create feature matrix for VAE training
feature_matrix = []
for preset in presets:
    # Focus on filter parameters for wobble analysis
    wobble_features = []
    for param_name, param_data in preset['parameters'].items():
        if param_data['category'] in ['filter', 'lfo']:
            wobble_features.append(param_data['normalized_value'])
    
    feature_matrix.append(wobble_features)

feature_matrix = np.array(feature_matrix)
# Ready for β-VAE training!

🎵 Use Cases

• Music Production: Analyze and modify synthesizer presets
• ML Research: Extract features for generative models
• Preset Management: Organize and categorize sound libraries
• Sound Design: Understand parameter relationships
• Academic Research: Study electronic music characteristics

📚 Documentation

Visit our wiki for detailed documentation.

🤝 Contributing

Contributions welcome! Please read our contributing guidelines.

📄 License

MIT License - see LICENSE file for details.

🎯 Citation

If you use this library in research, please cite:

@software{sysex_toolkit_2025,
  title={SysEx Toolkit: Universal SysEx Library for Music Production},
  author={Ovcharov, Vladimir},
  year={2025},
  url={https://github.com/SystematicLabs/sysex-toolkit}
}