  Domain shift is considered a challenge in machine learning as it causes
significant degradation of model performance. In the Acoustic Scene
Classification task (ASC), domain shift is mainly caused by different recording
devices. Several studies have already targeted domain generalization to improve
the performance of ASC models on unseen domains, such as new devices. Recently,
the Controllable Gate Adapter ConGater has been proposed in Natural Language
Processing to address the biased training data problem. ConGater allows
controlling the debiasing process at inference time. ConGater's main advantage
is the continuous and selective debiasing of a trained model, during inference.
In this work, we adapt ConGater to the audio spectrogram transformer for an
acoustic scene classification task. We show that ConGater can be used to
selectively adapt the learned representations to be invariant to device domain
shifts such as recording devices. Our analysis shows that ConGater can
progressively remove device information from the learned representations and
improve the model generalization, especially under domain shift conditions
(e.g. unseen devices). We show that information removal can be extended to both
device and location domain. Finally, we demonstrate ConGater's ability to
enhance specific device performance without further training.
