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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 jazz dataset

In the previous section, we introduced the tools that are necessary for building a dataset based on information contained in the MIDI files from the full LMD dataset. In this section, we'll delve deeper into building a custom dataset by using external APIs such as the Last.fm API.

In this section, we'll use the LMD-matched distribution since it is (partially) matched with the MSD containing metadata information that will be useful for us, such as artist and title. That metadata can then be used in conjunction with Last.fm to get the song's genre. We'll also be extracting drum and piano instruments, just like we did in the previous section.

The LMD extraction tools

Before we start...

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