  Acoustic scene classification and related tasks have been dominated by
Convolutional Neural Networks (CNNs). Top-performing CNNs use mainly audio
spectograms as input and borrow their architectural design primarily from
computer vision. A recent study has shown that restricting the receptive field
(RF) of CNNs in appropriate ways is crucial for their performance, robustness
and generalization in audio tasks. One side effect of restricting the RF of
CNNs is that more frequency information is lost. In this paper, we perform a
systematic investigation of different RF configuration for various CNN
architectures on the DCASE 2019 Task 1.A dataset. Second, we introduce
Frequency Aware CNNs to compensate for the lack of frequency information caused
by the restricted RF, and experimentally determine if and in what RF ranges
they yield additional improvement. The result of these investigations are
several well-performing submissions to different tasks in the DCASE 2019
Challenge.
