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 generating melodies, using both monophonic and polyphonic models.

We first started by looking at LSTM cells and their usage in RNNs to keep information for a long period of time, using forget, input, and output gates.

Then, we generated melodies with the Melody RNN, using multiple pre-trained models such as basic, lookback, and attention. We saw that the basic model cannot learn repeating structure, because its input vector encoding do not contain such information. We then looked at the lookback encoding, where step position in bar and repeating structure are encoded into the input vector, making it possible for the model to learn such information. We finally saw the attention model, where the attention mechanism makes it possible to look at multiple previous steps, using an attention mask that gives a weight to each step.

Finally, we generated...