Book Image

Data Augmentation with Python

By : Duc Haba
Book Image

Data Augmentation with Python

By: Duc Haba

Overview of this book

Data is paramount in AI projects, especially for deep learning and generative AI, as forecasting accuracy relies on input datasets being robust. Acquiring additional data through traditional methods can be challenging, expensive, and impractical, and data augmentation offers an economical option to extend the dataset. The book teaches you over 20 geometric, photometric, and random erasing augmentation methods using seven real-world datasets for image classification and segmentation. You’ll also review eight image augmentation open source libraries, write object-oriented programming (OOP) wrapper functions in Python Notebooks, view color image augmentation effects, analyze safe levels and biases, as well as explore fun facts and take on fun challenges. As you advance, you’ll discover over 20 character and word techniques for text augmentation using two real-world datasets and excerpts from four classic books. The chapter on advanced text augmentation uses machine learning to extend the text dataset, such as Transformer, Word2vec, BERT, GPT-2, and others. While chapters on audio and tabular data have real-world data, open source libraries, amazing custom plots, and Python Notebook, along with fun facts and challenges. By the end of this book, you will be proficient in image, text, audio, and tabular data augmentation techniques.
Table of Contents (17 chapters)
Part 1: Data Augmentation
Part 2: Image Augmentation
Part 3: Text Augmentation
Part 4: Audio Data Augmentation
Part 5: Tabular Data Augmentation


Audio filters help eliminate unwanted interference or noise from an audio recording. The result is to improve the tone and playback of human speech, music, nature, and environmental recordings.

The audio filter changes frequency by increasing, boosting, or amplifying a range of frequencies. A filter could also decrease, delete, cut, attenuate, or pass a frequency range. For example, using a low-pass filter, you could remove the traffic noise from a recording of two people talking in a city.

In particular, we will cover the following filters:

  • Low-pass filter
  • High-pass filter
  • Band-pass filter
  • Low-shelf filter
  • High-shelf filter
  • Band-stop filter
  • Peak filter

Let’s start with the low pass filter.

Low-pass filter

The low-pass filter cuts or deletes low-frequency sounds, such as traffic noise, machine engine rumbles, or elephant calls.

Typically, the minimum cut-off frequency is 150 Hz, the maximum cut-off is 7.5 kHz, the...