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Hands-On Transfer Learning with Python

You're reading from   Hands-On Transfer Learning with Python Implement advanced deep learning and neural network models using TensorFlow and Keras

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781788831307
Length 438 pages
Edition 1st Edition
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Authors (4):
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Nitin Panwar Nitin Panwar
Author Profile Icon Nitin Panwar
Nitin Panwar
Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
Tamoghna Ghosh Tamoghna Ghosh
Author Profile Icon Tamoghna Ghosh
Tamoghna Ghosh
Dipanjan Sarkar Dipanjan Sarkar
Author Profile Icon Dipanjan Sarkar
Dipanjan Sarkar
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Toc

Table of Contents (14) Chapters Close

Preface 1. Machine Learning Fundamentals FREE CHAPTER 2. Deep Learning Essentials 3. Understanding Deep Learning Architectures 4. Transfer Learning Fundamentals 5. Unleashing the Power of Transfer Learning 6. Image Recognition and Classification 7. Text Document Categorization 8. Audio Event Identification and Classification 9. DeepDream 10. Style Transfer 11. Automated Image Caption Generator 12. Image Colorization 13. Other Books You May Enjoy

Understanding audio event classification

By now, you should understand the basic task of classification or categorization, where we have structured or unstructured data that is usually labeled or annotated with specific groups or categories. The main task in automated classification is to build a model such that, with future data points, we can classify each data point or record into one of the specific categories based on various data attributes or features.

We have already looked at text and image classification in the previous chapters. In this chapter, we will look at classifying audio events. An audio event is basically an event or occurrence of an activity that is usually captured by an audio signal. Usually, short audio clips are used to represent audio events since, even if they are recurring, the sounds are usually similar. Sometimes, though, longer audio clips might...

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