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Hands-On Generative Adversarial Networks with PyTorch 1.x

You're reading from   Hands-On Generative Adversarial Networks with PyTorch 1.x Implement next-generation neural networks to build powerful GAN models using Python

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781789530513
Length 312 pages
Edition 1st Edition
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Authors (2):
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John Hany John Hany
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John Hany
Greg Walters Greg Walters
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Greg Walters
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Introduction to GANs and PyTorch FREE CHAPTER
2. Generative Adversarial Networks Fundamentals 3. Getting Started with PyTorch 1.3 4. Best Practices for Model Design and Training 5. Section 2: Typical GAN Models for Image Synthesis
6. Building Your First GAN with PyTorch 7. Generating Images Based on Label Information 8. Image-to-Image Translation and Its Applications 9. Image Restoration with GANs 10. Training Your GANs to Break Different Models 11. Image Generation from Description Text 12. Sequence Synthesis with GANs 13. Reconstructing 3D models with GANs 14. Other Books You May Enjoy

Speech quality enhancement with SEGAN

In Chapter 7, Image Restoration with GANs, we explored how GANs can restore some of the pixels in images. Researchers have found a similar application in NLP where GANs can be trained to get rid of the noises in audio in order to enhance the quality of the recorded speeches. In this section, we will learn how to use SEGAN to reduce background noise in the audio and make the human voice in the noisy audio more audible.

SEGAN architecture

Speech Enhancement GAN (SEGAN) was proposed by Santiago Pascual, Antonio Bonafonte, and Joan Serrà in their paper, SEGAN: Speech Enhancement Generative Adversarial Network. It uses 1D convolutions to successfully remove noise from speech audio. You...

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