  This paper introduces an ensemble of discriminators that improves the
accuracy of a domain adaptation technique for the localization of multiple
sound sources. Recently, deep neural networks have led to promising results for
this task, yet they require a large amount of labeled data for training.
Recording and labeling such datasets is very costly, especially because data
needs to be diverse enough to cover different acoustic conditions. In this
paper, we leverage acoustic simulators to inexpensively generate labeled
training samples. However, models trained on synthetic data tend to perform
poorly with real-world recordings due to the domain mismatch. For this, we
explore two domain adaptation methods using adversarial learning for sound
source localization which use labeled synthetic data and unlabeled real data.
We propose a novel ensemble approach that combines discriminators applied at
different feature levels of the localization model. Experiments show that our
ensemble discrimination method significantly improves the localization
performance without requiring any label from the real data.
