  Convolutional Neural Networks (CNNs) have been dominating classification
tasks in various domains, such as machine vision, machine listening, and
natural language processing. In machine listening, while generally exhibiting
very good generalization capabilities, CNNs are sensitive to the specific audio
recording device used, which has been recognized as a substantial problem in
the acoustic scene classification (DCASE) community. In this study, we
investigate the relationship between over-parameterization of acoustic scene
classification models, and their resulting generalization abilities.
Specifically, we test scaling CNNs in width and depth, under different
conditions. Our results indicate that increasing width improves generalization
to unseen devices, even without an increase in the number of parameters.
