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Data Augmentation with Python

You're reading from   Data Augmentation with Python Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data

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Product type Paperback
Published in Apr 2023
Publisher Packt
ISBN-13 9781803246451
Length 394 pages
Edition 1st Edition
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Author (1):
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Duc Haba Duc Haba
Author Profile Icon Duc Haba
Duc Haba
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1: Data Augmentation
2. Chapter 1: Data Augmentation Made Easy FREE CHAPTER 3. Chapter 2: Biases in Data Augmentation 4. Part 2: Image Augmentation
5. Chapter 3: Image Augmentation for Classification 6. Chapter 4: Image Augmentation for Segmentation 7. Part 3: Text Augmentation
8. Chapter 5: Text Augmentation 9. Chapter 6: Text Augmentation with Machine Learning 10. Part 4: Audio Data Augmentation
11. Chapter 7: Audio Data Augmentation 12. Chapter 8: Audio Data Augmentation with Spectrogram 13. Part 5: Tabular Data Augmentation
14. Chapter 9: Tabular Data Augmentation 15. Index 16. Other Books You May Enjoy

Real-world audio datasets

By now, you should be familiar with downloading Pluto and real-world datasets from the Kaggle website. We chose to download Pluto from Chapter 2 because the image augmentation functions shown in Chapters 3 and 4, and the text augmentation techniques shown in Chapters 5 and 6, are not beneficial for audio augmentation.

The three real-world audio datasets we will use are as follows:

  • The Musical Emotions Classification (MEC) real-world audio dataset from Kaggle contains 2,126 songs separated into train and test folders. They are instrumental music, and the goal is to predict happy or sad music. Each piece is about 9 to 10 minutes in length and is in *.wav format. It was published in 2020 and is available to the public. Its license is Attribution-ShareAlike 4.0 International (CC BY-SA 4.0): https://creativecommons.org/licenses/by-sa/4.0/.
  • The Crowd Sourced Emotional Multimodal Actors Dataset (CREMA-D) real-world audio dataset from Kaggle contains...
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