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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

Looking at existing datasets

In this chapter, we'll be preparing some data for training. Note that this will be covered in more detail in Chapter 7, Training Magenta Models. Preparing data and training models are two different activities that are done in tandem—first, we prepare the data, then train the models, and finally go back to preparing the data to improve our model's performance.

First, we'll start by looking at symbolic representations other than MIDI, such as MusicXML and ABCNotation, since Magenta also handles them, even if the datasets we'll be working with in this chapter will be in MIDI only. Then, we'll provide an overview of existing datasets, including datasets from the Magenta team that were used to train some models we've already covered. This overview is by no means exhaustive but can serve as a starting point when it comes...

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