Book Image

Hands-On Music Generation with Magenta

By : Alexandre DuBreuil
Book Image

Hands-On Music Generation with Magenta

By: Alexandre DuBreuil

Overview of this book

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 (16 chapters)
1
Section 1: Introduction to Artwork Generation
3
Section 2: Music Generation with Machine Learning
8
Section 3: Training, Learning, and Generating a Specific Style
11
Section 4: Making Your Models Interact with Other Applications

Summary

In this chapter, we've looked at Tensorflow.js and Magenta.js, the JavaScript implementations of TensorFlow and Magenta. We've learned that TensorFlow.js is GPU accelerated using WebGL and that Magenta.js has a limited set of models available that can only be used for generation, not training. We've converted a Python-trained model from the previous chapter to a format that TensorFlow.js can load. We've also introduced Tone.js and the Web Audio API, which is used by Magenta.js to synthesize sound in the browser.

Then, we've created three music generation web applications. The first application used GANSynth to sample short audio notes. By doing so, we've learned how to import the required scripts, either using a big ES5 bundle or a smaller, split up, ES6 bundle. The second application used MusicVAE to sample a trio of instruments, with the...