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It is a practical research topic how to deal with multi-device audio inputsby a single acoustic scene classification system with efficient design. In thiswork, we propose Residual Normalization, a novel feature normalization methodthat uses frequency-wise normalization % instance normalization with a shortcutpath to discard unnecessary device-specific information without losing usefulinformation for classification. Moreover, we introduce an efficientarchitecture, BC-ResNet-ASC, a modified version of the baseline architecturewith a limited receptive field. BC-ResNet-ASC outperforms the baselinearchitecture even though it contains the small number of parameters. Throughthree model compression schemes: pruning, quantization, and knowledgedistillation, we can reduce model complexity further while mitigating theperformance degradation. The proposed system achieves an average test accuracyof 76.3% in TAU Urban Acoustic Scenes 2020 Mobile, development dataset with315k parameters, and average test accuracy of 75.3% after compression to 61.0KBof non-zero parameters. The proposed method won the 1st place in DCASE 2021challenge, TASK1A.

Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization