  In many fields of research, labeled datasets are hard to acquire. This is
where data augmentation promises to overcome the lack of training data in the
context of neural network engineering and classification tasks. The idea here
is to reduce model over-fitting to the feature distribution of a small
under-descriptive training dataset. We try to evaluate such data augmentation
techniques to gather insights in the performance boost they provide for several
convolutional neural networks on mel-spectrogram representations of audio data.
We show the impact of data augmentation on the binary classification task of
surgical mask detection in samples of human voice (ComParE Challenge 2020).
Also we consider four varying architectures to account for augmentation
robustness. Results show that most of the baselines given by ComParE are
outperformed.
