Research on Traffic Acoustic Event Detection Algorithm Based on Model Fusion

Road traffic monitoring is important for intelligent transportation, and researchers have begun to focus on the detection of traffic events using acoustic information. In this paper, we apply model fusion to traffic acoustic event classification. First, an improved, two-channel convolutional neural network (CNN) model is proposed as the weak classifier for constructing the fusion model. The mel-cepstral feature and its first-order and second-order difference are selected as the input features. Six different input features are constructed after signal preprocessing and segmentation. Second, after training six different CNN models, the voting method and support vector machine (SVM) stacking method are used to construct the final fusion model. Experimental results demonstrate that the detection rate of traffic acoustic events reaches 95.1%, which is higher than that of traditional traffic detection algorithms.