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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

Product type Paperback

Published in Jan 2020

Publisher

ISBN-13 9781838824419

Length 360 pages

Edition 1st Edition

Languages

Python

Tools

Magenta

Concepts

Deep Learning

Author (1):

Alexandre DuBreuil

View More author details

Table of Contents (16) Chapters

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

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Questions

What are the advantages and disadvantages of the different symbolic representations?
Write a piece of code that will extract cello instruments from MIDI files.
How many rock songs are present in LMD? How many match one of the "jazz", "blues", "country" tags?
Write a piece of code that will extend MIDI files that are too short for the Melody RNN pipeline.
Extract the jazz drums from GMD. Can we train a quantized model with this?
Why is data augmentation important?

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Authors (1)

DuBreuil

DuBreuil

Alexandre DuBreuil is a software engineer and generative music artist. Through collaborations with bands and artists, he has worked on many generative art projects, such as generative video systems for music bands in concerts that create visuals based on the underlying musical structure, a generative drawing software that creates new content based on a previous artist's work, and generative music exhibits in which the generation is based on real-time events and data. Machine learning has a central role in his music generation projects, and Alexandre has been using Magenta since its release for inspiration, music production, and as the cornerstone for making autonomous music generation systems that create endless soundscapes.

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