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

DuBreuil

$19.99 per month

4 (3 Ratings)

Paperback Jan 2020 360 pages 1st Edition

DuBreuil

$19.99 per month

4 (3 Ratings)

Paperback Jan 2020 360 pages 1st Edition

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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

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Product Details

Publication date : Jan 31, 2020

Length: 360 pages

Edition : 1st

Language : English

ISBN-13 : 9781838824419

Vendor :

Google

Category :

Data

Languages :

Python

Concepts :

Deep Learning

Tools :

Magenta

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15 Chapters

Section 1: Introduction to Artwork Generation

Introduction to Magenta and Generative Art

Technical requirements

Overview of generative art

New techniques with machine learning

Google's Magenta and TensorFlow in music generation

Installing Magenta and Magenta for GPU

Installing the music software and synthesizers

Installing the code editing software

Generating a basic MIDI file

Summary

Questions

Further reading

Section 2: Music Generation with Machine Learning

Generating Drum Sequences with the Drums RNN

Technical requirements

The significance of RNNs in music generation

Using the Drums RNN on the command line

Using the Drums RNN in Python

Summary

Questions

Further reading

Generating Polyphonic Melodies

Technical requirements

LSTM for long-term dependencies

Generating melodies with the Melody RNN

Generating polyphony with the Polyphony RNN and Performance RNN

Summary

Questions

Further reading

Latent Space Interpolation with MusicVAE

Technical requirements

Continuous latent space in VAEs

Score transformation with MusicVAE and GrooVAE

Understanding TensorFlow code

Summary

Questions

Further reading

Audio Generation with NSynth and GANSynth

Technical requirements

Learning about WaveNet and temporal structures for music

Neural audio synthesis with NSynth

Using GANSynth as a generative instrument

Summary

Questions

Further reading

Section 3: Training, Learning, and Generating a Specific Style

Data Preparation for Training

Technical requirements

Looking at existing datasets

Building a dance music dataset

Building a jazz dataset

Preparing the data using pipelines

Summary

Questions

Further reading

Training Magenta Models

Technical requirements

Choosing the model and configuration

Training and tuning a model

Using Google Cloud Platform

Summary

Questions

Further reading

Section 4: Making Your Models Interact with Other Applications

Magenta in the Browser with Magenta.js

Technical requirements

Introducing Magenta.js and TensorFlow.js

Creating a Magenta.js web application

Making Magenta.js interact with other apps

Summary

Questions

Further reading

Making Magenta Interact with Music Applications

Technical requirements

Sending MIDI to a DAW or synthesizer

Looping the generated MIDI

Using Magenta as a standalone application with Magenta Studio

Summary

Questions

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Customer reviews

Rating distribution

(3 Ratings)

5 star 0%

4 star 100%

3 star 0%

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1 star 0%

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.

Amazon Verified review

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.

Amazon Verified review

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

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About the author

DuBreuil

Alexandre DuBreuil is a software engineer and generative music artist. Through collaborations with bands and artists, he has worked on many generative art projects, such as generative video systems for music bands in concerts that create visuals based on the underlying musical structure, a generative drawing software that creates new content based on a previous artist's work, and generative music exhibits in which the generation is based on real-time events and data. Machine learning has a central role in his music generation projects, and Alexandre has been using Magenta since its release for inspiration, music production, and as the cornerstone for making autonomous music generation systems that create endless soundscapes.

See other products by DuBreuil

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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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