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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

Exploratory analysis of audio events

We will follow a standard workflow of analyzing, visualizing, modeling, and evaluating our models on our audio data. Once all the data is downloaded, you will notice that there are a total of ten folders containing audio data samples in WAV format. We also have a metadata folder, which contains metadata information for each audio file in the UrbanSound8K.csv file. You can use this file to assign the class labels for each file or you can understand the file naming nomenclature to do the same.

Each audio file is named in a specific format. The name takes the [fsID]-[classID]-[occurrenceID]-[sliceID].wav format, which is populated as follows:

  • [fsID]: The freesound ID of the recording from which this excerpt (slice) is taken
  • [classID]: A numeric identifier of the sound class
  • [occurrenceID]: A numeric identifier to distinguish different occurrences...
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