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Introduction to Magenta and Generative Art

In this chapter, you'll learn the basics of generative music and what already exists. You'll learn about the new techniques of artwork generation, such as machine learning, and how those techniques can be applied to produce music and art. Google's Magenta open source research platform will be introduced, along with Google's open source machine learning platform TensorFlow, along with an overview of its different parts and the installation of the required software for this book. We'll finish the installation by generating a simple MIDI file on the command line.

The following topics will be covered in this chapter:

Overview of generative artwork
New techniques with machine learning
Magenta and TensorFlow in music generation
Installing Magenta
Installing the music software and synthesizers
Installing the code editing...

Key benefits

Learn how machine learning, deep learning, and reinforcement learning are used in music generation

Generate new content by manipulating the source data using Magenta utilities, and train machine learning models with it

Explore various Magenta projects such as Magenta Studio, MusicVAE, and NSynth

Description

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.

What you will learn

Use RNN models in Magenta to generate MIDI percussion, and monophonic and polyphonic sequences Use WaveNet and GAN models to generate instrument notes in the form of raw audio Employ Variational Autoencoder models like MusicVAE and GrooVAE to sample, interpolate, and humanize existing sequences Prepare and create your dataset on specific styles and instruments Train your network on your personal datasets and fix problems when training networks Apply MIDI to synchronize Magenta with existing music production tools like DAWs

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