Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification

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. We show that our method improves the target domain accuracy for both a toy dataset and an urban acoustic scenes dataset. We further compare our method to Maximum Mean Discrepancy-based DA and find it more robust to the choice of DA parameters. Our submission, based on this method, to DCASE 2019 Task 1b gave us the 4th place in the team ranking.20420