Hands-On Music Generation with Magenta: Explore the role of deep learning in music generation and assisted music composition

Design and use machine learning models for music generation using Magenta and make them interact with existing music creation tools.

Links

• Packt Publishing - Buy the book in ebook format or paperback
• Amazon Kindle or Amazon Paperback
• Source code - Python and JavaScript code for the examples in the book
• Code in Action - Videos that shows the code examples being executed and the resulting generation

Description

2020-01-31 - The importance of machine learning (ML) in art is growing at a rapid pace due to recent advancements in the field, and Magenta is at the forefront of this innovation. With this book, you’ll follow a hands-on approach to using ML models for music generation, learning how to integrate them into an existing music production workflow. Complete with practical examples and explanations of the theoretical background required to understand the underlying technologies, this book is the perfect starting point to begin exploring music generation.

The book will help you learn how to use the models in Magenta for generating percussion sequences, monophonic and polyphonic melodies in MIDI, and instrument sounds in raw audio. Through practical examples and in-depth explanations, you’ll understand ML models such as RNNs, VAEs, and GANs. Using this knowledge, you’ll create and train your own models for advanced music generation use cases, along with preparing new datasets. Finally, you’ll get to grips with integrating Magenta with other technologies, such as digital audio workstations (DAWs), and using Magenta.js to distribute music generation apps in the browser.

By the end of this book, you’ll be well-versed with Magenta and have developed the skills you need to use ML models for music generation in your own style.

Table of Contents

• Chapter 1: Introduction on Magenta and generative art
• Chapter 2: Generating drum sequences with Drums RNN
• Chapter 3: Generating polyphonic melodies
• Chapter 4: Latent space interpolation with Music VAE
• Chapter 5: Audio generation with GANSynth
• Chapter 6: Data Preparation for Training
• Chapter 7: Training Magenta models
• Chapter 8: Magenta in the browser with Magenta.js
• Chapter 9: Making Magenta interact with music applications