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Hands-On Music Generation with Magenta

Hands-On Music Generation with Magenta

By : DuBreuil
4 (3)
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Hands-On Music Generation with Magenta

Hands-On Music Generation with Magenta

4 (3)
By: 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)
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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

Looking at existing datasets

In this chapter, we'll be preparing some data for training. Note that this will be covered in more detail in Chapter 7, Training Magenta Models. Preparing data and training models are two different activities that are done in tandem—first, we prepare the data, then train the models, and finally go back to preparing the data to improve our model's performance.

First, we'll start by looking at symbolic representations other than MIDI, such as MusicXML and ABCNotation, since Magenta also handles them, even if the datasets we'll be working with in this chapter will be in MIDI only. Then, we'll provide an overview of existing datasets, including datasets from the Magenta team that were used to train some models we've already covered. This overview is by no means exhaustive but can serve as a starting point when it comes...

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