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

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 convoluti...

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Bibliographic Details
Published in:arXiv.org 2020-07
Main Authors: Zhao, Jingqiao, Zhen-Hua, Feng, Kong, Qiuqiang, Song, Xiaoning, Xiao-Jun, Wu
Format: Article
Language:English
Subjects:
Acoustics
Artificial neural networks
Classification
Complexity
Convolution
Neural networks
Online Access:Get full text
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Summary: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.
ISSN:2331-8422