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

Choosing the model and configuration

In Chapter 6, Data Preparation for Training, we looked at how to build a dataset. The datasets we produced were symbolic ones composed of MIDI files containing specific instruments, such as percussion or piano, and from specific genres, such as dance music and jazz music.

We also looked at how to prepare a dataset, which corresponds to the action of preparing the input formats (MIDI, MusicXML, or ABCNotation) into a format that can be fed to the network. That format is specific to a Magenta model, meaning the preparation will be different for the Drums RNN and MusicVAE models, even if both models can train on percussion data.

The first step before starting the training is to choose the proper model and configuration for our use case. Remember, a model in Magenta defines a deep neural network architecture, and each network type has its advantages...