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  • Produktbild: Build AI-Enhanced Audio Plugins with C++
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Build AI-Enhanced Audio Plugins with C++

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

21.06.2024

Abbildungen

100 SW-Abb., 44 SW-Fotos, 56 SW-Zeichn.

Verlag

Taylor & Francis

Seitenzahl

362

Maße (L/B/H)

25,4/17,8/2 cm

Gewicht

681 g

Sprache

Englisch

ISBN

978-1-03-243042-3

Beschreibung

Rezension

"This book is long overdue. With the explosion of activity in the field of AI-assisted music creation, the need for mastering all the chain of software from ideas to actual plugins is stronger than ever. Matthew has a direct, hands-on approach that not only will be of great help to people wanting to contribute to the field, but will also encourage others to experiment and share their code. Matthew's experience in teaching shows and definitely contributes to making the book easy to read and to-the-point."

François Pachet, Research Director

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

21.06.2024

Abbildungen

100 SW-Abb., 44 SW-Fotos, 56 SW-Zeichn.

Verlag

Taylor & Francis

Seitenzahl

362

Maße (L/B/H)

25,4/17,8/2 cm

Gewicht

681 g

Sprache

Englisch

ISBN

978-1-03-243042-3

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: GPSR Kontakt

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Die Leseprobe wird geladen.
  • Produktbild: Build AI-Enhanced Audio Plugins with C++
  • Produktbild: 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