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Data Labeling in Machine Learning with Python

You're reading from   Data Labeling in Machine Learning with Python Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781804610541
Length 398 pages
Edition 1st Edition
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Author (1):
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Vijaya Kumar Suda Vijaya Kumar Suda
Author Profile Icon Vijaya Kumar Suda
Vijaya Kumar Suda
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Table of Contents (18) Chapters Close

Preface 1. Part 1: Labeling Tabular Data
2. Chapter 1: Exploring Data for Machine Learning FREE CHAPTER 3. Chapter 2: Labeling Data for Classification 4. Chapter 3: Labeling Data for Regression 5. Part 2: Labeling Image Data
6. Chapter 4: Exploring Image Data 7. Chapter 5: Labeling Image Data Using Rules 8. Chapter 6: Labeling Image Data Using Data Augmentation 9. Part 3: Labeling Text, Audio, and Video Data
10. Chapter 7: Labeling Text Data 11. Chapter 8: Exploring Video Data 12. Chapter 9: Labeling Video Data 13. Chapter 10: Exploring Audio Data 14. Chapter 11: Labeling Audio Data 15. Chapter 12: Hands-On Exploring Data Labeling Tools 16. Index 17. Other Books You May Enjoy

Summary

In this chapter, we have delved into the fundamentals of audio data, including the concept of waveforms, sample rates, and the discrete nature of audio. These fundamentals provide the building blocks for audio analysis. We analyzed the difference between spectrograms and mel spectrograms in audio analysis and visualized how audio signals change over time and how they relate to human perception. Visualization is a powerful way to gain insights into the structure and characteristics of audio. With the knowledge and techniques gained in this chapter, we are better equipped to explore the realms of speech recognition, music classification, and countless other applications where sound takes center stage.

In the next chapter, we will learn how to label audio data using CNNs and speech recognition using the Whisper model and Azure Cognitive Services.

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