Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization
arXiv:2111.06531
Abstract
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.
Proceedings of the Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021)
References in corpus (5)
- Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling
- Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation
- Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutions
- Low-complexity acoustic scene classification for multi-device audio: analysis of DCASE 2021 Challenge systems
- Position-based Scaled Gradient for Model Quantization and Pruning