Data Augmentation and Deep Learning in Audio Classification Problems: Alignment Between Training and Test Environments

The global COVID-19 pandemic increased the interest in automatic analysis and classification of cough sounds. However, developing such a system requires a large dataset of expert-labelled cough sounds, which remains elusive. Data augmentation techniques are often employed to train machine learning models with limited data, while ensuring model robustness to real-world variations. We have recently proposed the use of “natural” spectral data augmentation methods, including noise and reverberation [1]. In this paper, we further investigate the method by studying the alignment between training and testing environments. We augment training sound data with varying levels of reverberation and Gaussian noise, and evaluate augmented convolutional neural network models across a range of test environments on two audio classification tasks: speech command recognition and cough sound classification. Results demonstrate the broad robustness of the data-augmented deep learning model across various test environments. Furthermore, the augmented model's performance on cough classification is compared to 4 expert annotations on a large cough dataset and is seen to be near-human-level accuracy. This augmented model is recommended for classifying cough sounds in natural settings beyond laboratory conditions.