  It is a practical research topic how to deal with multi-device audio inputs
by a single acoustic scene classification system with efficient design. In this
work, we propose Residual Normalization, a novel feature normalization method
that uses frequency-wise normalization % instance normalization with a shortcut
path to discard unnecessary device-specific information without losing useful
information for classification. Moreover, we introduce an efficient
architecture, BC-ResNet-ASC, a modified version of the baseline architecture
with a limited receptive field. BC-ResNet-ASC outperforms the baseline
architecture even though it contains the small number of parameters. Through
three model compression schemes: pruning, quantization, and knowledge
distillation, we can reduce model complexity further while mitigating the
performance degradation. 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. The proposed method won the 1st place in DCASE 2021
challenge, TASK1A.
