- If you want to generate a musical score, what do you train your model to do?
- What are the properties that are interesting in RNNs concerning music prediction?
- Given an RNN hidden layer with the notation h(t + 2), what two inputs is the hidden layer getting?
- Given the following parameters for the generation, --num_steps 32 and --qpm 80, how long will the generated MIDI be in seconds? How many bars will it be?
- What happens if you increase --branch_factor and increase --temperature during the generation phase?
- How many nodes will the beam search algorithm go through at the last iteration for a generation of 3 steps with the --branch_factor 4 and --beam_size 2 parameters?
- What is the Protobuf Message class that is used in Magenta to represent a sequence of MIDI notes? (NoteSequence)
- Using the one-hot encoding described in the encoding section, what is the encoded vector...
Hands-On Music Generation with Magenta
By :
Hands-On Music Generation with Magenta
By:
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
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