Location-invariant representations for acoustic scene classification

High intra-class variance is one of the significant challenges in solving the problem of acoustic scene classification. This work identifies the recording location (or city) of an audio sample as a source of intra-class variation. We overcome this variation by utilising multi-view learning, where each recording location is considered as a view. Canonical correlation analysis (CCA) based multi-view algorithms learn a subspace where samples from the same class are brought together, and samples from different classes are moved apart, irrespective of the views. By considering cities as views, and by using several variants of CCA algorithms, we show that intra-class variation can be reduced, and location-invariant representations can be learnt. The proposed method demonstrates an improvement of more than 8% on the DCASE 2018 and 2019 datasets, when compared to not using the view information.