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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

The significance of RNNs in music generation

Specific neural network architectures are designed for specific problems. It doesn't mean that one architecture is better than another one—it just means it is better at a specific task.

In this section, we'll be looking at our specific problem, generating music, and see why RNNs are well suited for the task. We'll be building our knowledge of neural network architectures for music throughout this book, by introducing specific concepts in each chapter.

For music generation, we are looking at two specific problems that RNNs solve—operating on sequences in terms of input and output and keeping an internal state of past events. Let's have a look at those properties.

Musical score prediction is analogous to generating music. By predicting the next notes from an input sequence, you can iteratively generate...
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