DD-CNN: Depthwise Disout Convolutional Neural Network for Low-complexity Acoustic Scene Classification
Sound
2020-07-28 v1 Machine Learning
Audio and Speech Processing
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.
Cite
@article{arxiv.2007.12864,
title = {DD-CNN: Depthwise Disout Convolutional Neural Network for Low-complexity Acoustic Scene Classification},
author = {Jingqiao Zhao and Zhen-Hua Feng and Qiuqiang Kong and Xiaoning Song and Xiao-Jun Wu},
journal= {arXiv preprint arXiv:2007.12864},
year = {2020}
}