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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

Using the Drums RNN on the command line

Now that we understand how RNNs make for powerful tools of music generation, we'll use the Drums RNN model to do just that. The pre-trained models in Magenta are a good way of starting music generation straightaway. For the Drums RNN model, we'll be using the drum_kit pre-trained bundle, which was trained on thousands of percussion MIDI files.

This section will provide insight into the usage of Magenta on the command line. We'll be primarily using Python code to call Magenta, but using the command line has some advantages:

  • It is simple to use and useful for quick use cases.
  • It doesn't require writing any code or having any programming knowledge.
  • It encapsulates parameters in helpful commands and flags.

In this section, we'll use the Drums RNN model in the command line and learn to configure the generation though...

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