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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

Audio Spectrogram

Before dissecting the Spectrogram, let’s review the fundamental differences between a Spectrogram and a Waveform plot. The Spectrogram graphs show the frequency components of a sound signal over time, focusing on frequency and intensity. In contrast, the Waveforms concentrate on the timing and amplitude of sounds. The difference is in the visual representation of the sound wave. The underlying data representation and the transformation methods are the same.

An audio Spectrogram is another visual representation of a sound wave, and you saw the Waveform graph in Chapter 7. The _draw_spectrogram() helper method uses the Librosa library to import the audio file and convert it into an amplitude data one-dimensional array and a sampling rate in Hz. The next step is to use the Matplotlib library to draw the Spectrogram plot. Likewise, Pluto takes the output from the Librosa library function and uses the Matplotlib function to draw the fancy blue and yellow Waveform...

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