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

Data Preparation for Training

So far, we've used existing Magenta pre-trained models since they are quite powerful and easy to use. But training our own models is crucial since it allows us to generate music in a specific style or generate specific structures or instruments. Building and preparing a dataset is the first step before training our own model. To do that, we need to look at existing datasets and APIs that will help us to find meaningful data. Then, we need to build two datasets in MIDI for specific styles—dance and jazz. Finally, we will need to prepare the MIDI files for training using data transformations and pipelines.

The following topics will be covered in this chapter:

  • Looking at existing datasets
  • Building a dance music dataset
  • Building a jazz dataset
  • Preparing the data using pipelines