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

Standard audio augmentation techniques

Similar to image augmentation in Chapter 3, various audio libraries provide many more functions than are necessary for augmentation. Therefore, we will only cover some of the methods available in the chosen audio library.

In image augmentation, the term safe level is defined as not altering or distorting the original image beyond an acceptable level. There is no standard terminology for deforming the original audio signal beyond a permissible point. Thus, the term safe or true will be used interchangeably to denote a limit point for the audio signal.

Fun challenge

Here is a thought experiment: all audio files are represented as numbers in time series format. Thus, can you create a statistically valid augmentation method that does not consider human hearing perception? In other words, use math to manipulate a statistically valid number array, but never listen to the before and after effects. After all, audio augmentation aims to have more...