Research on Urban Audio Classification Based on Residual Neural Network
Duling Xv, Yang Li · 2021 International Conference on Computer Engineering and Application (ICCEA) · 2021
In recent years, audio classification has been extensively studied, and the classification of urban sounds has great application requirements in criminal investigation and environmental protection. In this paper, a multi-feature hybrid description method is used to classify target city sounds with a multi-layer residual network structure. Firstly, a plurality of feature extraction results were compared with a conventional single feature. Secondly, different network models are studied, and their performance under different characteristics is tested and compared. Finally, comparing Resnet and multi-layer perceptrons, it is found that the Resnet50v2 method under mixed features has a better classification effect on the Ubansound8k data set, reaching 90.7%.