Environmental Sound Classification Method Based on Two-Stream Lightweight Convolutional Neural Network

Abstract Neural networks have achieved success in the task of environmental sound classification. However, the traditional neural network model has too many parameters and high computational cost. The lightweight networks solve these problems by compressing parameters, but reduce the classification accuracy. To solve the problems in existing research, we propose a two-stream model based on two lightweight convolutional neural networks, called TSLCNN-DS, which saves memory and improves the classification performance of environmental sounds. Specifically, we first used data patching and data balancing to slightly expand the amount of experimental data. Then we designed two lightweight and efficient classification networks based on the attention mechanism and residual learning. Finally, the Dempster-Shafer evidence theory is used to fuse the output of the two networks, and the two-stream model is integrated. Experiments have shown that the model has achieved a classification accuracy of 97.44% on the UrbanSound8k dataset, using only 0.12 M parameters.