Improving Sound Event Classification by Increasing Shift Invariance in Convolutional Neural Networks
Recent studies have put into question the commonly assumed shift invariance property of convolutional networks, showing that small shifts in the input can affect the output predictions substantially. In this paper, we ask whether lack of shift invariance is a problem in sound event classification, and whether there are benefits in addressing it. Specifically, we evaluate two pooling methods to improve shift invariance in CNNs, based on low pass filtering and adaptive sampling of incoming feature maps. These methods are implemented via small architectural modifications inserted into the pooling layers of CNNs. We evaluate the effect of these architectural changes on the FSD50K dataset using models of different capacity and in presence of strong regularization. We show that these modifications consistently improve sound event classification in all cases considered, without adding any (or adding very few) trainable parameters, which makes them an appealing alternative to conventional pooling layers. The outcome is a new state of the art mAP of 0.541 on the FSD50K classification benchmark.
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