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

Score transformation with MusicVAE and GrooVAE

In the previous chapters, we've learned to generate various parts of a score. We've generated percussion and monophonic and polyphonic melodies and learned about expressive timing. This section builds on that foundation and shows how to manipulate the generated scores and transform them. In our example, we'll sample two small scores from the latent space, we'll then interpolate between the two samples (progressively going from the first sample to the second sample), and finally, we'll add some groove (or humanization, see the following information box for more information) on the resulting score.

For our example, we'll work on percussion since adding groove in MusicVAE only works on drums. We'll be using different configurations and pre-trained models in MusicVAE to perform the following steps. Remember...