Feature projection-based unsupervised domain adaptation for acoustic scene classification

The mismatch between the data distributions of training and test data acquired under different recording conditions and using different devices is known to severely impair the performance of acoustic scene classification (ASC) systems. To address this issue, we propose an unsupervised domain adaptation method for ASC based on the projection of spectro-temporal features extracted from both the source and target domain onto the principal subspace spanned by the eigenvectors of the sample covariance matrix of source-domain training data. Using the TUT Urban Acoustic Scenes 2018 Mobile Development dataset we show that the proposed method outperforms state-of-the-art unsupervised domain adaptation techniques when applied jointly with a convolutional ASC model and can also be practically employed as a feature extraction procedure for shallower artificial neural networks.