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

Training and tuning a model

Training a machine model is an empirical and iterative approach, where we first prepare the data and the configuration, then train the model, fail, and restart again. Getting models to train on the first try is rare, but we'll persevere through hardship together.

When launching a training phase, we'll be looking at specific metrics to verify that our model is training properly and converging. We'll also be launching an evaluation phase, which executes on a separate, smaller dataset, to verify that the model can properly generalize on data that it hasn't seen yet.

The evaluation dataset is often called the validation dataset in machine learning in general, but we'll keep the term evaluation since it is used in Magenta.

The validation dataset is different than the test dataset, which is an external dataset, often curated by hand...