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Hands-On Music Generation with Magenta

Hands-On Music Generation with Magenta

By : DuBreuil
4 (3)
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Hands-On Music Generation with Magenta

Hands-On Music Generation with Magenta

4 (3)
By: 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)
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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

Technical requirements

In this chapter, we'll use the following tools:

  • A command line or Bash to launch Magenta from the Terminal
  • Python and its libraries
  • The Python multiprocessing module for multi-threaded data preparation
  • Matplotlib to plot our data preparation results
  • Magenta to launch data pipeline conversion
  • MIDI, ABCNotation, and MusicXML as data formats
  • External APIs such as Last.fm

In Magenta, we'll make use of data pipelines. We will explain these in depth later in this chapter, but if you feel like you need more information, the pipeline README file in Magenta's source code (github.com/tensorflow/magenta/tree/master/magenta/pipelines) is a good place to start. You can also take a look at Magenta's code, which is well documented. There's also additional content in the Further reading section.

The code for this chapter can be found in this book...

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