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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 in Python

In the previous section, we've seen how much we can already do on the command line with the Drums RNN model. In this section, you'll get to create a small application that will use that model to generate music in Python.

Using Magenta in Python is a bit difficult because of the following reasons:

  • It requires you to write code and understand Magenta's architecture.
  • It requires more boilerplate code and is less straightforward.

But it also has advantages that we think are important:

  • You have more freedom in the usage of the models.
  • You can create new models and modify existing ones.
  • You can go beyond generating single sequences.

The last point is important for us because we'll be building a small music application that generates music autonomously. Calling Magenta's scripts on the command line is convenient, but you cannot...

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