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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

Audio Generation with NSynth and GANSynth

In this chapter, we'll be looking into audio generation. We'll first provide an overview of WaveNet, an existing model for audio generation, especially efficient in text-to-speech applications. In Magenta, we'll use NSynth, a WaveNet autoencoder model, to generate small audio clips that can serve as instruments for a backing MIDI score. NSynth also enables audio transformations such as scaling, time stretching, and interpolation. We'll also use GANSynth, a faster approach based on Generative Adversarial Network (GAN).

The following topics will be covered in this chapter:

  • Learning about WaveNet and temporal structures for music
  • Neural audio synthesis with NSynth
  • Using GANSynth as a generative instrument
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