Research on structural sound source localization method by neural network

Abstract

To solve problems related to much calculation to adapt to complex scenes in traditional structural sound source localization, this paper proposes a method based on neural network. The structural sound source at other positions was stimulated by successively striking 36 grid centers on the surface of the plate. The time delay between different accelerometer signals was considered as the input, and the location of the predicted sound source was considered as the output. The influence of the number of test sets and epoch training times on sound source localization accuracy was discussed. These results show that with the increase in the epoch training times, the number of test set decreases, and the number of training set increases, increasing the sound source localization accuracy of backpropagation neural network. However, these error conditions will frequently appear due to the overfitting phenomenon. When the epoch is trained to 50,000 times, and the quantity of the test set is 4, the backpropagation neural network has the best localization accuracy with an order of magnitude of 10−3 in error, and the localization error scope of the plate is between 0.01 and 0.1 m.