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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

Neural audio synthesis with NSynth

In this section, we'll be combining different audio clips together. We'll learn to encode the audio, optionally saving the resulting encodings on disk, mix (add) them, and then decode the added encodings to retrieve a sound clip.

We'll be handling 1-second audio clips only. There are two reasons for this: first, handling audio is costly, and second, we want to generate instrument notes in the form of short audio clips. The latter is interesting for us because we can then sequence the audio clips using MIDI generated by the models we've been using in the previous chapters. In that sense, you can view NSynth as a generative instrument, and the previous models, such as MusicVAE or Melody RNN, as a generative score (partition) composer. With both elements, we can generate full tracks, with audio and structure.

To generate sound...

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