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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

Mel-spectrogram and Chroma STFT plots

Pluto spends additional time plotting various Spectrograms because the augmentation technique is the same as in the Waveform graph in Chapter 7. Pluto will write fewer new wrapper functions. He will reuse the methods from the previous chapter, but before that, let’s draw more Spectrograms.

The subjective unit of pitch, also known as the mel scale, is a pitch unit with equal distance between pitches. S. S. Stevens, John Volkmann, and E. B. Newmann proposed the mel scale in the scholarly paper titled, A scale for the measurement of the psychological magnitude of pitch, in 1937.

The math calculation for the mel scale is complex. Thus, Pluto relies on the melspectrogram() method from the Librosa library to perform the computation. The Pluto draw_melspectrogram() wrapper method uses the Librosa melspectrogram() function, and the code snippet is as follows:

# code snippeet for the melspectrogram
mel = librosa.feature.melspectrogram(y...
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