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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
  • The Python multiprocessing module for multi-threaded data preparation
  • Matplotlib to plot our data preparation results
  • Magenta to launch data pipeline conversion
  • MIDI, ABCNotation, and MusicXML as data formats
  • External APIs such as Last.fm

In Magenta, we'll make use of data pipelines. We will explain these in depth later in this chapter, but if you feel like you need more information, the pipeline README file in Magenta's source code (github.com/tensorflow/magenta/tree/master/magenta/pipelines) is a good place to start. You can also take a look at Magenta's code, which is well documented. There's also additional content in the Further reading section.

The code for this chapter can be found in this book...

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