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Generative AI with Python and TensorFlow 2

You're reading from   Generative AI with Python and TensorFlow 2 Create images, text, and music with VAEs, GANs, LSTMs, Transformer models

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800200883
Length 488 pages
Edition 1st Edition
Languages
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Authors (2):
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Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
Joseph Babcock Joseph Babcock
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Joseph Babcock
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Toc

Table of Contents (16) Chapters Close

Preface 1. An Introduction to Generative AI: "Drawing" Data from Models 2. Setting Up a TensorFlow Lab FREE CHAPTER 3. Building Blocks of Deep Neural Networks 4. Teaching Networks to Generate Digits 5. Painting Pictures with Neural Networks Using VAEs 6. Image Generation with GANs 7. Style Transfer with GANs 8. Deepfakes with GANs 9. The Rise of Methods for Text Generation 10. NLP 2.0: Using Transformers to Generate Text 11. Composing Music with Generative Models 12. Play Video Games with Generative AI: GAIL 13. Emerging Applications in Generative AI 14. Other Books You May Enjoy
15. Index

Music generation using GANs

In the previous section, we tried our hand at music generation using a very simple LSTM-based model. Now, let's raise the bar a bit and try to see how we can generate music using a GAN. In this section, we will leverage the concepts related to GANs that we have learned in the previous chapters and apply them to generating music.

We've already seen that music is continuous and sequential in nature. LSTMs or RNNs in general are quite adept at handling such datasets. We have also seen that, over the years, various types of GANs have been proposed to train deep generative networks efficiently.

Combining the power of LSTMs and GAN-based generative networks, Mogren et al. presented Continuous Recurrent Neural Networks with Adversarial Training: C-RNN-GAN4 in 2016 as a method for music generation. This is a straightforward yet effective implementation for music generation. As in the previous section, we will keep things simple and focus only...

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