  Semi-supervised learning and domain adaptation techniques have drawn
increasing attention in the field of domestic sound event detection thanks to
the availability of large amounts of unlabeled data and the relative ease to
generate synthetic strongly-labeled data. In a previous work, several
semi-supervised learning strategies were designed to boost the performance of a
mean-teacher model. Namely, these strategies include shift consistency training
(SCT), interpolation consistency training (ICT), and pseudo-labeling. However,
adversarial domain adaptation (ADA) did not seem to improve the event detection
accuracy further when we attempt to compensate for the domain gap between
synthetic and real data. In this research, we empirically found that ICT tends
to pull apart the distributions of synthetic and real data in t-SNE plots.
Therefore, ICT is abandoned while SCT, in contrast, is applied to train both
the student and the teacher models. With these modifications, the system
successfully integrates with an ADA network, and we achieve 47.2% in the F1
score on the DCASE 2020 task 4 dataset, which is 2.1% higher than what was
reported in the previous work.
