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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

New techniques with machine learning

Machine learning is important for computer science because it allows complex functions to be modeled without them being explicitly written. Those models are automatically learned from examples, instead of being manually defined. This has a huge implication for arts in general since explicitly writing the rules of a painting or a musical score is inherently difficult.

In recent years, the advent of deep learning has propelled machine learning to new heights in terms of efficiency. Deep learning is especially important for our use case of music generation since using deep learning techniques doesn't require a preprocessing step of feature extraction, which is necessary for classical machine learning and hard to do on raw data such as image, text, and you guessed it audio. In other words, traditional machine learning algorithms...

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