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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

Building a dance music dataset

Now that we have datasets available so that we can build our own dataset, we'll look at different ways of using the information contained in a MIDI file. This section will serve as an introduction to the different tools that can be used for dataset creation using only MIDI files. In this section, we'll use the LMD-full distribution.

In the next section, Building a jazz dataset, we will delve deeper into using external information.

Threading the execution to handle large datasets faster

When building datasets, we want our code to execute fast because of the amount of data we'll be handling. In Python, using threading and multiprocessing is one way to go. There are many ways of...

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