Build AI-Enhanced Audio Plugins with C++

Build AI-Enhanced Audio Plugins with C++ explains how to embed artificial intelligence technology inside tools that can be used by audio and music professionals, through worked examples using Python, C++ and audio APIs which demonstrate how to combine technologies to produce professional, AI-enhanced creative tools.

Alongside a freely accessible source code repository created by the author that accompanies the book for readers to reference, each chapter is supported by complete example applications and projects, including an autonomous music improviser, a neural network-based synthesizer meta-programmer and a neural audio effects processor. Detailed instructions on how to build each example are also provided, including source code extracts, diagrams and background theory.

This is an essential guide for software developers and programmers of all levels looking to integrate AI into their systems, as well as educators and students of audio programming, machine learning and software development.

Part 1: Getting started  1. Introduction to the book  2. Setting up your development environment  3. Installing JUCE  4. Installing and using CMake  5. Set up libtorch  6. Python setup instructions  7. Common development environment setup problems  8. Basic plugin development  9. FM synthesizer plugin  Part 2: ML-powered plugin control: the meta-controller  10. Using regression for synthesizer control  11. Experiment with regression and libtorch  12. The meta-controller  13. Linear interpolating Superknob  14. Untrained Torchknob  15. Training the torchknob  16. Plugin meta-controller  17. Placing plugins in an AudioProcessGraph structure  18. Show a plugin’s user interface  19. From plugin host to meta-controller  Part 3: The autonomous music improviser  20. Background: all about sequencers  21. Programming with Markov models  22. Starting the Improviser plugin  23. Modelling note onset times  24. Modelling note duration  25. Polyphonic Markov model  Part 4: Neural audio effects  26. Welcome to neural effects  27. Finite Impulse Responses, signals and systems  28. Convolution  29. Infinite Impulse Response filters  30. Waveshapers  31. Introduction to neural guitar amplifier emulation  32. Neural FX: LSTM network  33. JUCE LSTM plugin  34. Training the amp emulator: dataset  35. Data shapes, LSTM models and loss functions  36. The LSTM training loop  37. Operationalising the model in a plugin  38. Faster LSTM using RTNeural  39. Guide to the projects in the repository.

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Build AI-Enhanced Audio Plugins with C++