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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

Understanding TensorFlow code

In this section, we'll take a quick look at the TensorFlow code to understand a bit more how the sampling, interpolating, and humanizing code works. This will also make references to the first section of this chapter, Continuous latent space in VAEs, so that we make sense of both the theory and the hands-on practice we've had.

But first, let's do an overview of the model's initialization code. For this section, we'll take the cat-drums_2bar_small configuration as an example and the same model initialization code we've been using for this chapter, meaning batch_size of 8.

Building the VAE graph

We'll start by looking at the TrainedModel constructor in the models...

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