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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

Summary

In this chapter, we've looked at Tensorflow.js and Magenta.js, the JavaScript implementations of TensorFlow and Magenta. We've learned that TensorFlow.js is GPU accelerated using WebGL and that Magenta.js has a limited set of models available that can only be used for generation, not training. We've converted a Python-trained model from the previous chapter to a format that TensorFlow.js can load. We've also introduced Tone.js and the Web Audio API, which is used by Magenta.js to synthesize sound in the browser.

Then, we've created three music generation web applications. The first application used GANSynth to sample short audio notes. By doing so, we've learned how to import the required scripts, either using a big ES5 bundle or a smaller, split up, ES6 bundle. The second application used MusicVAE to sample a trio of instruments, with the...

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