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Hands-On Music Generation with Magenta

You're reading from   Hands-On Music Generation with Magenta Explore the role of deep learning in music generation and assisted music composition

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Product type Paperback
Published in Jan 2020
Publisher
ISBN-13 9781838824419
Length 360 pages
Edition 1st Edition
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Author (1):
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Alexandre DuBreuil Alexandre DuBreuil
Author Profile Icon Alexandre DuBreuil
Alexandre DuBreuil
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Table of Contents (16) Chapters Close

Preface 1. Section 1: Introduction to Artwork Generation
2. Introduction to Magenta and Generative Art FREE CHAPTER 3. Section 2: Music Generation with Machine Learning
4. Generating Drum Sequences with the Drums RNN 5. Generating Polyphonic Melodies 6. Latent Space Interpolation with MusicVAE 7. Audio Generation with NSynth and GANSynth 8. Section 3: Training, Learning, and Generating a Specific Style
9. Data Preparation for Training 10. Training Magenta Models 11. Section 4: Making Your Models Interact with Other Applications
12. Magenta in the Browser with Magenta.js 13. Making Magenta Interact with Music Applications 14. Assessments 15. Other Books You May Enjoy

Technical requirements

In this chapter, we'll use the following tools:

  • Command line or Bash to launch Magenta from the terminal
  • Python and Magenta to convert trained models for Magenta.js
  • TensorFlow.js and Magenta.js to create music generation apps in the browser
  • JavaScript, HTML, and CSS to write Magenta.js web applications
  • A recent browser (Chrome, Firefox, Edge, Safari) for up-to-date web APIs
  • Node.js and npm to install Magenta.js and its dependencies server side
  • FluidSynth to listen to generated MIDI from the browser

In Magenta.js, we'll make the use of the Music RNN and MusicVAE models for MIDI sequence generation and GANSynth for audio generation. We'll cover their usage in depth, but if you feel like you need more information, the Magenta.js Music README in the Magenta.js source code (github.com/tensorflow/magenta-js/tree/master/music) is a good place...

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