Build AI-Enhanced Audio Plugins with C++

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