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

Continuous latent space in VAEs

In Chapter 2, Generating Drum Sequences with the Drums RNN, we saw how we can use an RNN (LSTM) and a beam search to iteratively generate a sequence, by taking an input and then predicting, note by note, which next note is the most probable. That enabled us to use a primer as a basis for the generation, using it to set a starting melody or a certain key.

Using that technique is useful, but it has its limitations. What if we wanted to start with a primer and explore variations around it, and not just in a random way, but in a desired specific direction? For example, we could have a two-bars melody for a bass line, and we would like to hear how it sounds when played more as an arpeggio. Another example would be transitioning smoothly between two melodies. This is where the RNN models we previously saw fall short and where VAEs comes into play.

Before...