Recognition of abnormal car door noise based on multi-scale feature fusion

Abstract

To accurately identify the abnormal door-closing noise, we propose a method to recognize the time-frequency image of door closing sound based on a multi-scale feature fusion network model. The door-closing sound signal is transformed into a time-frequency image through wavelet analysis, and a classification model based on multi-scale feature fusion is designed. The model introduces multi-scale filters and dilated convolution and adds two improved inception modules to keep the model lightweight. At the same time, richer spatial features can be obtained. The features of different scales are spliced and input to the fully connected layer, and a dropout layer is added to the fully connected layer to suppress overfitting. By comparing the loss and accuracy rate, adjusting different hyperparameters, the optimal model is obtained. The experimental results show that the multi-scale feature fusion network model has a higher accuracy rate than the transfer learning model. Test accuracy rate is 86% and can effectively recognize abnormal door-closing noise. It provides a feasible theoretical basis for the direction of abnormal door noise recognition.