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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

Chapter 8: Magenta in the Browser with Magenta.js

  1. We can train models using TensorFlow.js, but we cannot train models using Magenta.js. We need to train the models in Magenta using Python and import the resulting models in Magenta.js.
  2. The Web Audio API enables audio synthesis in the browser using audio nodes for generation, transformation, and routing. The easiest way to use it is to use an audio framework such as Tone.js.
  3. The method is randomSample and the argument is the pitch of the generated note. As an example, using 60 will result in a single note at MIDI pitch 60, or C4 in letter notation. This is also useful as a reference for pitching the note up or down using Tone.js.

  1. The method is sample and the number of instruments depends on the model that is being used. In our example, we've used the trio model, which generates three instruments. Using a melody model will...
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