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 looked at how to build and prepare a dataset that will be used for training. First, we looked at existing datasets and explained how some are more suitable than others for a specific use case. We then looked at the LMD and the MSD, which are useful for their size and completeness, and datasets from the Magenta team, such as the MAESTRO dataset and the GMD. We also looked at external APIs such as Last.fm, which can be used to enrich existing datasets.

Then, we built a dance music dataset and used information contained in MIDI files to detect specific structures and instruments. We learned how to compute our results using multiprocessing and how to plot statistics about the resulting MIDI files.

After, we built a jazz dataset by extracting information from the LMD and using the Last.fm API to find the genre of each song. We also looked at how to find...