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You're reading from Hands-On Music Generation with Magenta Explore the role of deep learning in music generation and assisted music composition

Product type Paperback

Published in Jan 2020

Publisher

ISBN-13 9781838824419

Length 360 pages

Edition 1st Edition

Languages

Python

Tools

Magenta

Concepts

Deep Learning

Author (1):

Alexandre DuBreuil

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Table of Contents (16) Chapters

Preface

1. Section 1: Introduction to Artwork Generation

2. Introduction to Magenta and Generative Art FREE CHAPTER

3. Section 2: Music Generation with Machine Learning

4. Generating Drum Sequences with the Drums RNN

5. Generating Polyphonic Melodies

6. Latent Space Interpolation with MusicVAE

7. Audio Generation with NSynth and GANSynth

8. Section 3: Training, Learning, and Generating a Specific Style

9. Data Preparation for Training

10. Training Magenta Models

11. Section 4: Making Your Models Interact with Other Applications

12. Magenta in the Browser with Magenta.js

13. Making Magenta Interact with Music Applications

14. Assessments

15. Other Books You May Enjoy

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Audio Signals in Python: An article on plotting audio signals in Python, explaining how to create a CQT plot (myinspirationinformation.com/uncategorized/audio-signals-in-python/)
Constant-Q transform toolbox for music processing: A paper (2010) on implementing CQTs for music (www.researchgate.net/publication/228523955_Constant-Q_transform_toolbox_for_music_processing)
WaveNet: A generative model for raw audio: A DeepMind article on WaveNet models for raw audio (deepmind.com/blog/article/wavenet-generative-model-raw-audio)
WaveNet: A Generative Model for Raw Audio: A WaveNet paper (2016) (arxiv.org/abs/1609.03499)
SampleRNN: An article explaining the differences between WaveNet and SampleRNN (deepsound.io/samplernn_first.html)
NSynth: Neural Audio Synthesis: A Magenta article on the NSynth model (magenta.tensorflow.org/nsynth)
Making a Neural Synthesizer Instrument...

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Authors (1)

DuBreuil

Alexandre DuBreuil is a software engineer and generative music artist. Through collaborations with bands and artists, he has worked on many generative art projects, such as generative video systems for music bands in concerts that create visuals based on the underlying musical structure, a generative drawing software that creates new content based on a previous artist's work, and generative music exhibits in which the generation is based on real-time events and data. Machine learning has a central role in his music generation projects, and Alexandre has been using Magenta since its release for inspiration, music production, and as the cornerstone for making autonomous music generation systems that create endless soundscapes.

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