The Neural Networks Performance Improvement Using Pink Noise Augmentation

Abstract

Data augmentation techniques artificially generate different versions of a real dataset to increase its size. Many practices have shown an increase in the accuracy of machine learning models after applying them. When training a machine learning model, it functions as a regularizer and helps to reduce overfitting. This work will discuss the improved neural network performance with the pink noise data augmentation in order to model system drifts.