Preface

The place of machine learning in art is becoming more and more strongly established because of recent advancements in the field. Magenta is at the forefront of that innovation. This book provides a hands-on approach to machine learning models for music generation and demonstrates 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.

In Hands-On Music Generation with Magenta, you'll learn how to use models in Magenta to generate percussion sequences, monophonic and polyphonic melodies in MIDI, and instrument sounds in raw audio. We'll be seeing plenty of practical examples and in-depth explanations of machine learning models, such as Recurrent Neural Networks (RNNs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs). Leveraging that knowledge, we'll be creating and training our own models for advanced music generation use cases, and we'll be tackling the preparation of new datasets. Finally, we'll be looking at integrating Magenta with other technologies, such as Digital Audio Workstations (DAWs), and using Magenta.js to distribute music generation applications in the browser.

By the end of this book, you'll be proficient in everything Magenta has to offer and equipped with sufficient knowledge to tackle music generation in your own style.