The place of machine learning in art is becoming more and more strongly established because of recent advancements in the field. Magenta is at the forefront of that innovation. This book provides a hands-on approach to machine learning models for music generation and demonstrates 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.
In Hands-On Music Generation with Magenta, you'll learn how to use models in Magenta to generate percussion sequences, monophonic and polyphonic melodies in MIDI, and instrument sounds in raw audio. We'll be seeing plenty of practical examples and in-depth explanations of machine learning models, such as Recurrent Neural Networks (RNNs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs). Leveraging that knowledge, we'll be creating and training our own models for advanced music generation use cases, and we'll be tackling the preparation of new datasets. Finally, we'll be looking at integrating Magenta with other technologies, such as Digital Audio Workstations (DAWs), and using Magenta.js to distribute music generation applications in the browser.
By the end of this book, you'll be proficient in everything Magenta has to offer and equipped with sufficient knowledge to tackle music generation in your own style.
Hands-On Music Generation with Magenta
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Hands-On Music Generation with Magenta
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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)
Preface
Section 1: Introduction to Artwork Generation
Free Chapter
Introduction to Magenta and Generative Art
Section 2: Music Generation with Machine Learning
Generating Drum Sequences with the Drums RNN
Generating Polyphonic Melodies
Latent Space Interpolation with MusicVAE
Audio Generation with NSynth and GANSynth
Section 3: Training, Learning, and Generating a Specific Style
Data Preparation for Training
Training Magenta Models
Section 4: Making Your Models Interact with Other Applications
Magenta in the Browser with Magenta.js
Making Magenta Interact with Music Applications
Assessments
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Customer Reviews