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Developing Kaggle Notebooks

You're reading from   Developing Kaggle Notebooks Pave your way to becoming a Kaggle Notebooks Grandmaster

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781805128519
Length 370 pages
Edition 1st Edition
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Author (1):
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Gabriel Preda Gabriel Preda
Author Profile Icon Gabriel Preda
Gabriel Preda
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Table of Contents (14) Chapters Close

Preface 1. Introducing Kaggle and Its Basic Functions FREE CHAPTER 2. Getting Ready for Your Kaggle Environment 3. Starting Our Travel – Surviving the Titanic Disaster 4. Take a Break and Have a Beer or Coffee in London 5. Get Back to Work and Optimize Microloans for Developing Countries 6. Can You Predict Bee Subspecies? 7. Text Analysis Is All You Need 8. Analyzing Acoustic Signals to Predict the Next Simulated Earthquake 9. Can You Find Out Which Movie Is a Deepfake? 10. Unleash the Power of Generative AI with Kaggle Models 11. Closing Our Journey: How to Stay Relevant and on Top 12. Other Books You May Enjoy
13. Index

Formats for signal data

Several competitions on Kaggle used sound data as an addition to regular tabular features. There were three competitions organized by Cornell Lab of Ornithology’s BirdCLEF (LifeCLEF Bird Recognition Challenge) in 2021, 2022, and 2023 for predicting a bird species from samples of bird songs (see Reference 4 for an example of one of these competitions). The format used in these competitions was .ogg. The .ogg format is used to store audio data with less bandwidth. It is considered technically superior to the .mp3 format.

We can read these types of file formats using the librosa library (see Reference 5). The following code can be used to load an .ogg file and display the sound wave:

import matplotlib.pyplot as plt
import librosa
def display_sound_wave(sound_path=None,
               text="Test", 
               color="green"):
    """
    Display a sound wave
    Args
        sound_path: path to the sound file...
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