Teaching Audio Developers How to Build AI-Enhanced Audio Plugins

Overview

Discover a workflow for developing AI-enhanced audio plugins using C++, JUCE, CMake, PyTorch, and RTNeural, designed for educators and developers interested in integrating AI into real-time audio applications.

Syllabus

• Introduction to AI-Enhanced Audio Plugins
Overview of AI in Audio Development
Introduction to Audio Plugins
• Setting Up the Development Environment
Installing and Configuring JUCE
Working with CMake for Building Applications
Integrating PyTorch for AI Capabilities
• Fundamentals of Audio Plugin Development
Audio Signal Processing Basics
Building and Testing Simple JUCE Plugins
• Introduction to Machine Learning with PyTorch
Basics of Neural Networks
Building and Training Audio ML Models
• Real-Time Audio Processing with RTNeural
Overview of RTNeural for Real-Time Neural Networks
Implementing Neural Networks in Real-Time Environments
• Combining AI with Audio Plugins
Integrating ML Models into JUCE Plugins
Optimizing AI Models for Real-Time Performance
• Advanced C++ Techniques for Audio Developers
Leveraging Modern C++ Features
Performance Optimization Strategies for Audio
• Case Studies and Applications
Analyzing AI-Enhanced Audio Plugins
Best Practices and Lessons Learned
• Testing and Deployment of AI Audio Plugins
Unit Testing and Debugging
Preparing Plugins for Multiple Platforms
• Future Trends in AI and Audio Technology
New Developments and Emerging Technologies
Innovations in AI for Audio Applications
• Course Project
Designing and Implementing an AI-Enhanced Audio Plugin
Peer Review and Feedback Sessions
• Conclusion and Next Steps
Resources for Continued Learning
Advice on Further Development and Innovation in AI Audio Plugins

Subjects

Computer Science