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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:

  • A command line or bash to launch Magenta from the Terminal
  • Python and its libraries to write music generation code using Magenta
  • Magenta to generate music in MIDI
  • MuseScore or FluidSynth to listen to the generated MIDI

In Magenta, we'll make the use of the MusicVAE and GrooVAE models. We'll be explaining these models in depth, but if you feel like you need more information, the model's README in Magenta's source code (github.com/tensorflow/magenta/tree/master/magenta/models) is a good place to start. You can also take a look at Magenta's code, which is well documented. We also provide additional content in the last section, Further reading.

The code for this chapter is in this book's code GitHub in the Chapter04 folder, located at github.com/PacktPublishing/hands-on-music...

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