#Preset Analyzers - shepherdvovkes/idmlatentspace GitHub Wiki
- IDM Latent Space Wiki
- Clone the repository
- Install dependencies
- Run analysis
The IDM Latent Space project transforms high-dimensional synthesizer preset data into compact, machine learning-friendly representations. By analyzing Access Virus and Osiris synthesizer presets, the system creates reduced-dimensional spaces (32D, 64D, 128D) that preserve musical characteristics while enabling AI applications.
| Section | Description | Key Pages |
|---|---|---|
| Technical Documentation | Implementation details and API reference | API Reference, Data Structures, Analysis Pipeline |
| User Guide | Getting started and usage examples | Installation Guide, Basic Usage, Advanced Features |
| Academic Background | Research foundation and theory | KubMLOps Papers, Latent Space Theory, Related Work |
| Development | Contributing and extending the project | Contributing Guide, Code Standards, Testing |
- 2025-01-21 - Initial release with Osiris preset support
- 2025-01-21 - Added comprehensive MediaWiki documentation
- 2025-01-21 - Released KubMLOps academic papers
- 2025-01-21 - Implemented 32D/64D/128D dimensionality reduction
- Real-time preset analysis API
- Web-based visualization dashboard
- Additional synthesizer format support
- Generative model integration
- Prepare Files - Place `osiris_preset.syx` and `osiris_all_presets.syx` in project directory
- Run Analyzer - Execute `python updated_preset_analyzer.py`
- Review Results - Check generated JSON, NPY, and TXT files
- Load in ML - Import NumPy vectors for machine learning applications
Synthesizer presets are collections of parameter settings that define a sound. The IDM Latent Space project focuses on:
- Access Virus - Popular virtual analog synthesizer with 384 parameters
- Osiris - Virus-compatible synthesizer with similar parameter structure
- SysEx Format - System Exclusive MIDI data containing preset information
Latent space refers to a compressed representation that captures the essential characteristics of the original data:
| Original Space | Latent Space | Compression Ratio |
|---|---|---|
| 384 parameters | 32 dimensions | 8.3% (12:1) |
| 384 parameters | 64 dimensions | 16.7% (6:1) |
| 384 parameters | 128 dimensions | 33.3% (3:1) |
Parameters are weighted by musical significance:
- Music Information Retrieval - Analyze large preset databases
- Generative Modeling - Train VAEs/GANs for preset generation
- Classification Tasks - Automatic genre and style detection
- Clustering Analysis - Discover preset families and relationships
- Preset Interpolation - Morph between different sounds
- Smart Recommendations - Find similar presets automatically
- Educational Tools - Understand synthesizer programming principles
- DAW Integration - Import analysis results into music production workflows
- Model Training - Use vectors as input features for ML models
- Similarity Search - Find presets with similar characteristics
- Dimensionality Visualization - Plot preset relationships in 2D/3D
- Data Augmentation - Generate synthetic training data
SysEx Files → Parser → Parameters → Comparison → Weighting → Reduction → Output
↓ ↓ ↓ ↓ ↓ ↓ ↓
osiris_*.syx Raw Bytes Normalized Differences Importance Vectors Files
The project is supported by comprehensive academic research documented in the KubMLOps paper series:
- KubMLOps Volume 3 - Distributed audio processing with Kubernetes
- KubMLOps Volume 4 - Latent space applications for electronic music
- Machine learning operations in audio processing
- Kubernetes-based distributed computing for music analysis
- Mathematical frameworks for dimensionality reduction
- Evaluation metrics for preset similarity and generation
We welcome contributions from the community! Ways to get involved:
- Bug Reports - Submit issues on GitHub
- Feature Requests - Propose new functionality
- Code Contributions - Submit pull requests
- Documentation - Improve wiki content
- Research - Share findings and applications
- GitHub Issues - Technical questions and bug reports
- Wiki Discussions - Community questions and knowledge sharing
- Academic Collaboration - Research partnerships and citations
This project is released under the MIT License, allowing free use, modification, and distribution with attribution.
- Magenta - Google's music generation research
- NSynth - Neural audio synthesis dataset
- Essentia - Audio analysis library
- Librosa - Python audio analysis toolkit
- ArXiv Audio Processing - Latest research papers
- ISMIR Conference - Music information retrieval research
- Audio Engineering Society - Professional audio research
- Computer Music Journal - Academic publication
- Native Instruments - Professional music software and synthesizer development
- Splice - AI-powered music creation platform and sample library
- LANDR - Automated mastering service with ML algorithms
- Endel - Adaptive ambient music generation using AI
- Access Virus C - Hardware synthesizer with preset compatibility (Vintage Synth Explorer, Wikipedia)
- DSI Prophet 08 - Analog synthesizer with parameter automation (Manual PDF, Wikipedia)
- DSP56300 - OSIRIS - Virus emulation for Mac systems (GitHub, BPB Article)
- Universal Audio SPARK - AI-powered music production assistant
- Ableton Live 12 - Digital audio workstation with ML features
- Max for Live - Visual programming environment for Ableton Live
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Category:Machine Learning Category:Electronic Music Category:Audio Analysis Category:Synthesizers Category:Research Projects