Book Image

Hands-On Music Generation with Magenta

By : Alexandre DuBreuil
Book Image

Hands-On Music Generation with Magenta

By: Alexandre 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)
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

Chapter 7: Training Magenta Models

  1. See chapter_07_example_03.py.
  2. A network that underfits is a network that hasn't reached its optimum, meaning it won't predict well with the evaluation data, because it fits poorly the training data (for now). It can be fixed by letting it train long enough, by adding more network capacity, and more data.

  1. A network that overfits is a network that has learned to predict the input but cannot generalize to values outside of its training set. It can be fixed by adding more data, by reducing the network capacity, or by using regularization techniques such as dropout.
  2. Early stopping.
  3. Read On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima, which explains that a larger batch size leads to sharp minimizers, which in turn leads to poorer generalization. Therefore it is worse in terms of efficiency, but might...