QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design

• URL: http://arxiv.org/abs/2206.13909v1
• Date: Tue, 28 Jun 2022 11:42:52 GMT
• Title: QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design
• Authors: Byeonggeun Kim, Seunghan Yang, Jangho Kim, Simyung Chang
• Abstract summary: 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. 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.
• Score: 11.412720572948087
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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