DD-CNN: Depthwise Disout Convolutional Neural Network for Low-complexity Acoustic Scene Classification

• URL: http://arxiv.org/abs/2007.12864v1
• Date: Sat, 25 Jul 2020 06:02:20 GMT
• Title: DD-CNN: Depthwise Disout Convolutional Neural Network for Low-complexity Acoustic Scene Classification
• Authors: Jingqiao Zhao, Zhen-Hua Feng, Qiuqiang Kong, Xiaoning Song, Xiao-Jun Wu
• Abstract summary: This paper presents a Depthwise Disout Convolutional Neural Network (DD-CNN) for the detection and classification of urban acoustic scenes. We use log-mel as feature representations of acoustic signals for the inputs of our network. In the proposed DD-CNN, depthwise separable convolution is used to reduce the network complexity.
• Score: 29.343805468175965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a Depthwise Disout Convolutional Neural Network (DD-CNN) for the detection and classification of urban acoustic scenes. Specifically, we use log-mel as feature representations of acoustic signals for the inputs of our network. In the proposed DD-CNN, depthwise separable convolution is used to reduce the network complexity. Besides, SpecAugment and Disout are used for further performance boosting. Experimental results demonstrate that our DD-CNN can learn discriminative acoustic characteristics from audio fragments and effectively reduce the network complexity. Our DD-CNN was used for the low-complexity acoustic scene classification task of the DCASE2020 Challenge, which achieves 92.04% accuracy on the validation set.

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