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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.

Who is this book for?

This book is for technically inclined artists and musically inclined computer scientists. Readers who want to get hands-on with building generative music applications that use deep learning will also find this book useful. Although prior musical or technical competence is not required, basic knowledge of the Python programming language is assumed.

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

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Johannes Pich Apr 29, 2021

Ich arbeite gerade mit dem Buch an meiner Bachelorarbeit und finde es super, dass man die ganzen Code-teile ganz einfach aus der Docu kopieren kann, das macht das parallele Anwenden des Code sehr angenehm.Da ich leider wenig Vorwissen zu Python habe, muss ich mir viel nebenbei anlesen, einige Äußerungen sind auch etwas kryptisch, es werden Begriffe eingeführt und erst zwei Seiten später erklärt - also das geht noch besser :) Vermutlich liest sich das noch besser, wenn man schon Vorerfahrung mit Programmieren hat.Und es sind auch ein paar Rechtschreibfehler drin, die könnte man in der nächsten Ausgabe dann auch mal korrigieren.Ansonsten super Buch und toll, dass es eine Anleitung gibt, mit der man ein selbstlernendes Kompositionssystem bauen kann!

Amazon Verified review

Javonnii Curry Jan 12, 2021

Audio engineer/ Data ScientistThe layout of the book is amazing. I liked how the book starts with familiarizing the reader with the pre-trained models and their configurations. I enjoyed the in-depth explanation of "one-hot encoding" sequences and the changes depending on the model. The python files are clean and very well documented. I was able to train two separate Melody RNN models one w/ basic config and the other w/ attention config. I deployed a simple Magenta.js application with the base model and had fun generating melodies with the trained model"attention config" in Ableton Live. The coolest experience overall was playing a melody in Ableton and receiving a response "generated melody" from my trained model via Magenta midi interface.Some headaches:* I had trouble setting up the conda environment by the book on a "2020 mac Catalina." I successfully created the environment and installed the dependencies following the second branch on the git repo "magenta-master" for magenta 2.1.2. If you're on a new computer I would recommend using the branch mentioned above to follow along with the book.* The coding videos were helpful to see someone using the command line. I would have liked to see a few video walkthroughs "with audio" on the chapters Magenta.js and in-depth interaction with magenta midi and Ableton.Findings:I trained a Melody RNN w/ attention config but wasn't able to initialize it on the browser with Magenta.js. This is a current inability and Magenta is tracking this issue. I was able to initialize the model I trained with the base config.

Eric Nichols Nov 15, 2020

I'm a former Google employee and musician and have been following the Magenta research project since its inception. I am often surprised by how many questions show up on the public Magenta mailing list from musicians and programming hobbyists who are struggling with the basics of music generation using the open-source Magenta code. The Google team is responsive to questions, but it seems that many people have difficulty getting the basics working. This book fills that gap and provides something like a detailed "user's guide" to Magenta, along with a general overview/explanation of the music generation techniques used in Magenta.Specific topics include how to use the recurrent neural network (RNN) models in Magenta to generate monophonic music, polyphonic music, or drum sequences, how to use variational autoencoders (MusicVAE and GrooVAE) to control music generation, and using WaveNet and Generative Adversarial Networks (GANs) for generation of raw audio. Magenta.js is introduced for using Magenta in a web browser. Along the way the book introduces concepts such as convenient music representations for generation, public datasets such as the Million Song Dataset, and how to train custom Magenta models.The book is quite practical, with concrete code examples and instructions on the myriad issues that arise when tying to get Magenta to work, such as needing to install GPU drivers, setting up python conda environments, preprocessing MIDI files and converting to Google's "protobuf" format for use with Tensorflow, hooking up FluidSynth to listen generated MIDI, etc. Some of these instructions may go out of date over time, but for now they seem quite useful for people with less experience in software engineering, deep learning, or audio software.A pleasant surprise was the inclusion of a small number of exercises at the end of each chapter (e.g., "what are the advantages/disadvantages of different symbolic music representations?"), which I could see as helpful in preparing a specialized university seminar on music generation (answers are at the back of the book, so these questions are also useful for self-study).The book only provides a cursory look at the algorithms involved in music generation, so for details the reader will need to refer to the original research papers and blog posts from the Magenta team. It does provide a basic introduction, which might be all that is needed for the target audience of people who just want to start generating music.Two items seem to be notably missing from this book: 1) discussion of Coconet, the Magenta team model behind the Bach-generating "Google Doodle" in March 2019, and 2) discussion of Transformer models such as Music Transformer. I think we can blame Google for the latter, as the Music Transformer code doesn't yet appear as part fo the Magenta open source code base for some reason. The first is more surprising, but perhaps it will be added in a second edition of this book.Overall, I think this book does a great job filling in the gaps for users getting started with Magenta and music generation, and I highly recommend it for that audience. The ideal reader of the book already has some programming experience with python; I think a musician new to coding would still have a difficult challenge even with this book as a guide, but the highly-motivated hobbyist would probably have success.My main criticism is a bit superficial: I don't like the design of Packt press publications. The typesetting looks too much like a generic Microsoft Word document using default setting. The table of contents in particular is simply ugly and looks like standard self-published fare, and the font used for section headings is uninspiring. The greyscale figures in the print version are hard to read and don't do justice to the color figures in the PDF version of the book. Usually I prefer print books to ebooks, but this might be a case where I prefer the opposite, just because of the quality of the figures. I have enjoyed the content of a couple recent Packt publications, but I would encourage the publisher to improve on presentation in the future.

Hands-On Music Generation with Magenta: Explore the role of deep learning in music generation and assisted music composition

What do you get with Print?

Hands-On Music Generation with Magenta

Introduction to Magenta and Generative Art

Technical requirements

Overview of generative art

Pen and paper generative music

New techniques with machine learning

Google's Magenta and TensorFlow in music generation

Creating a music generation system

Installing Magenta and Magenta for GPU

Installing the music software and synthesizers

Installing the FluidSynth software synthesizer

Installing the code editing software

Installing Jupyter Notebook (optional)

Generating a basic MIDI file

Summary

Questions

Further reading

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Hands-On Music Generation with Magenta: Explore the role of deep learning in music generation and assisted music composition

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