  The performance of machine learning algorithms is known to be negatively
affected by possible mismatches between training (source) and test (target)
data distributions. In fact, this problem emerges whenever an acoustic scene
classification system which has been trained on data recorded by a given device
is applied to samples acquired under different acoustic conditions or captured
by mismatched recording devices. To address this issue, we propose an
unsupervised domain adaptation method that consists of aligning the first- and
second-order sample statistics of each frequency band of target-domain acoustic
scenes to the ones of the source-domain training dataset. This model-agnostic
approach is devised to adapt audio samples from unseen devices before they are
fed to a pre-trained classifier, thus avoiding any further learning phase.
Using the DCASE 2018 Task 1-B development dataset, we show that the proposed
method outperforms the state-of-the-art unsupervised methods found in the
literature in terms of both source- and target-domain classification accuracy.
