Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Apr 2023
Publisher Packt
ISBN-13 9781803246451
Length 394 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Duc Haba Duc Haba
Author Profile Icon Duc Haba
Duc Haba
Arrow right icon
View More author details
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 Data Augmentation with Spectrogram

In the previous chapter, we visualized the sound using the Waveform graph. An audio spectrogram is another visualizing method for seeing the audio components. The inputs to the Spectrogram are a one-dimensional array of amplitude values and the sampling rate. They are the same inputs as the Waveform graph.

An audio spectrogram is sometimes called a sonograph, sonogram, voiceprint, or voicegram. The Spectrogram is a more detailed representation of sound than the Waveform graph. It shows a correlation between frequency and amplitude (loudness) over time, which helps visualize the frequency content in a signal. Spectrograms make it easier to identify musical elements, detect melodic patterns, recognize frequency-based effects, and compare the results of different volume settings. Additionally, the Spectrogram can be more helpful in identifying non-musical aspects of a signal, such as noise and interference from other frequencies.

The typical...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at AU $24.99/month. Cancel anytime