Low-Complexity Acoustic Scene Classification Using Time Frequency Separable Convolution

Abstract

Replacing 2D-convolution operations by depth-wise separable time and frequency convolutions greatly reduces the number of parameters while maintaining nearly equivalent performances in the context of acoustic scene classification. In our experiments, the models’ sizes can be reduced by 6 to 14 times with similar performances. For a 3-class audio classification, replacing 2D-convolution in a CNN model gives roughly a 2% increase in accuracy. In a 10-class audio classification with multiple recording devices, replacing 2D-convolution in Resnet only reduces around 1.5% of the accuracy.