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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

Introducing Magenta.js and TensorFlow.js

In the previous chapters, we've covered Magenta in Python, its usage, and its inner workings. We'll now be looking at Google's Magenta.js, a smaller implementation of Magenta in JavaScript. Magenta and Magenta.js both have their advantages and disadvantages; let's compare them to see which one we should use, depending on the use case.

A Magenta.js application is easy to use and deploy since it executes in the browser. Developing and deploying a web application is easy: all you need is an HTML file and a web server, and your application is available for the whole world to see and use. This is a major advantage of making a browser-based application, since not only does it enable us to create our own music generation application easily, but it also makes it easy to use it collaboratively. See the Further reading section...

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