  Distribution mismatches between the data seen at training and at application
time remain a major challenge in all application areas of machine learning. We
study this problem in the context of machine listening (Task 1b of the DCASE
2019 Challenge). We propose a novel approach to learn domain-invariant
classifiers in an end-to-end fashion by enforcing equal hidden layer
representations for domain-parallel samples, i.e. time-aligned recordings from
different recording devices. No classification labels are needed for our domain
adaptation (DA) method, which makes the data collection process cheaper.
