  In classification tasks, the classification accuracy diminishes when the data
is gathered in different domains. To address this problem, in this paper, we
investigate several adversarial models for domain adaptation (DA) and their
effect on the acoustic scene classification task. The studied models include
several types of generative adversarial networks (GAN), with different loss
functions, and the so-called cycle GAN which consists of two interconnected GAN
models. The experiments are performed on the DCASE20 challenge task 1A dataset,
in which we can leverage the paired examples of data recorded using different
devices, i.e., the source and target domain recordings. The results of
performed experiments indicate that the best performing domain adaptation can
be obtained using the cycle GAN, which achieves as much as 66% relative
improvement in accuracy for the target domain device, while only 6\% relative
decrease in accuracy on the source domain. In addition, by utilizing the paired
data examples, we are able to improve the overall accuracy over the model
trained using larger unpaired data set, while decreasing the computational cost
of the model training.
