DD-CNN：用于低复杂度声学场景分类的深度离散卷积神经网络

本文提出了一种深度离散卷积神经网络（DD-CNN），用于城市声场的检测和分类。具体来说，我们使用log-mel作为网络输入的声音信号的特征表示。..

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 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.