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Vanishing gradients (values get multiplied by small values in each RNN step) and exploding gradients are common RNN problems that occur when training during the backpropagation step. LSTM provides a dedicated cell state that is modified by forget, input, and output gates to alleviate those problems.
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Gated recurrent units (GRUs) are simpler but less expressive memory cells, where the forget and input gates are combined into a single update gate.
- For a 3/4 time signature, you need 3 steps per quarter note, times 4 steps per quarter note, which equals 12 steps per bar. For a binary step counter to count to 12, you need 5 bits (like for 4/4 time) that will only count to 12. For 3 lookbacks, you'll need to look at the past 3 bars, with each bar being 12 steps, so you have [36, 24, 12].
- The resulting vector is the sum of the previous...
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.
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Current Title:
Hands-On Music Generation with Magenta
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
Other Books You May Enjoy
Customer Reviews