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 Data Augmentation with Spectrogram

In the previous chapter, we visualized the sound using the Waveform graph. An audio spectrogram is another visualizing method for seeing the audio components. The inputs to the Spectrogram are a one-dimensional array of amplitude values and the sampling rate. They are the same inputs as the Waveform graph.

An audio spectrogram is sometimes called a sonograph, sonogram, voiceprint, or voicegram. The Spectrogram is a more detailed representation of sound than the Waveform graph. It shows a correlation between frequency and amplitude (loudness) over time, which helps visualize the frequency content in a signal. Spectrograms make it easier to identify musical elements, detect melodic patterns, recognize frequency-based effects, and compare the results of different volume settings. Additionally, the Spectrogram can be more helpful in identifying non-musical aspects of a signal, such as noise and interference from other frequencies.

The typical...