  This technical report describes the details of our TASK1A submission of the
DCASE2021 challenge. The goal of the task is to design an audio scene
classification system for device-imbalanced datasets under the constraints of
model complexity. This report introduces four methods to achieve the goal.
First, we propose Residual Normalization, a novel feature normalization method
that uses instance normalization with a shortcut path to discard unnecessary
device-specific information without losing useful information for
classification. Second, we design an efficient architecture, BC-ResNet-Mod, a
modified version of the baseline architecture with a limited receptive field.
Third, we exploit spectrogram-to-spectrogram translation from one to multiple
devices to augment training data. Finally, we utilize three model compression
schemes: pruning, quantization, and knowledge distillation to reduce model
complexity. The proposed system achieves an average test accuracy of 76.3% in
TAU Urban Acoustic Scenes 2020 Mobile, development dataset with 315k
parameters, and average test accuracy of 75.3% after compression to 61.0KB of
non-zero parameters. We extend this work to [1].
