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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

Continuous latent space in VAEs

In Chapter 2, Generating Drum Sequences with the Drums RNN, we saw how we can use an RNN (LSTM) and a beam search to iteratively generate a sequence, by taking an input and then predicting, note by note, which next note is the most probable. That enabled us to use a primer as a basis for the generation, using it to set a starting melody or a certain key.

Using that technique is useful, but it has its limitations. What if we wanted to start with a primer and explore variations around it, and not just in a random way, but in a desired specific direction? For example, we could have a two-bars melody for a bass line, and we would like to hear how it sounds when played more as an arpeggio. Another example would be transitioning smoothly between two melodies. This is where the RNN models we previously saw fall short and where VAEs comes into play.

Before...

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