DD-CNN: Depthwise Disout Convolutional Neural Network for Low-complexity Acoustic Scene Classification
This addresses efficient acoustic scene detection for urban environments, but it is incremental as it builds on existing techniques like depthwise separable convolution and data augmentation.
The paper tackled low-complexity acoustic scene classification by proposing a Depthwise Disout Convolutional Neural Network (DD-CNN), achieving 92.04% accuracy on the DCASE2020 Challenge validation set.
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.