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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

Summary

As we saw from the beginning, audio augmentation is a challenging topic without hearing the audio recording in question, but we can visualize the techniques’ effect using Waveform graphs and zoom-in charts. Still, there is no substitution for listening to the before and after augmentation recordings. You have access to the Python Notebook with the complete code and audio button to play the augmented and original recordings.

First, we discussed the theories and concepts of an audio file. The three fundamental components of an audio file are amplitude, frequency, and sampling rate. The measurements of unit for frequency are Hertz (Hz) and Kilohertz (kHz). Pitch is similar to frequency, but the unit of measurement is the decibel (dB). Similarly, bit rate and bit depth are other forms expressing the sampling rate.

Next, we explained the standard audio augmentation techniques. The three essentials are time stretching, time shifting, and pitch scaling. The others are...

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