  It is well known that the mismatch between training (source) and test
(target) data distribution will significantly decrease the performance of
acoustic scene classification (ASC) systems. To address this issue, domain
adaptation (DA) is one solution and many unsupervised DA methods have been
proposed. These methods focus on a scenario of single source domain to single
target domain. However, we will face such problem that test data comes from
multiple target domains. This problem can be addressed by producing one model
per target domain, but this solution is too costly. In this paper, we propose a
novel unsupervised multi-target domain adaption (MTDA) method for ASC, which
can adapt to multiple target domains simultaneously and make use of the
underlying relation among multiple domains. Specifically, our approach combines
traditional adversarial adaptation with two novel discriminator tasks that
learns a common subspace shared by all domains. Furthermore, we propose to
divide the target domain into the easy-to-adapt and hard-to-adapt domain, which
enables the system to pay more attention to hard-to-adapt domain in training.
The experimental results on the DCASE 2020 Task 1-A dataset and the DCASE 2019
Task 1-B dataset show that our proposed method significantly outperforms the
previous unsupervised DA methods.
