  Data augmentation is an inexpensive way to increase training data diversity
and is commonly achieved via transformations of existing data. For tasks such
as classification, there is a good case for learning representations of the
data that are invariant to such transformations, yet this is not explicitly
enforced by classification losses such as the cross-entropy loss. This paper
investigates the use of training objectives that explicitly impose this
consistency constraint and how it can impact downstream audio classification
tasks. In the context of deep convolutional neural networks in the supervised
setting, we show empirically that certain measures of consistency are not
implicitly captured by the cross-entropy loss and that incorporating such
measures into the loss function can improve the performance of audio
classification systems. Put another way, we demonstrate how existing
augmentation methods can further improve learning by enforcing consistency.
