Application of a Deep Neural Network for Acoustic Source Localization Inside a Cavitation Tunnel

Abstract

Navigating with low noise is the key capability in the submarine design considerations, and noise reduction is also one of the most critical issues in the related fields. Therefore, it is necessary to identify the source of noise during design stage to improve the survivability of the submarines. The main objective of this research is using the supervised neural network to construct the system of noise localization to identify noise source in the large acoustic tunnel. Firstly, we started our proposed method by improving the Yangzhou’s method and Shunsuke’s method. In the test results, we find that the errors of the both can be reduced by using the min-max normalization to highlight the data characteristics of the low amplitude in some frequency. And Yangzhou’s method has higher accuracy than Shunsuke’s method. Then, we reset the diagonal numbers of the cross spectral matrix in Yangzhou’s method to zero and replace mean absolute error to be the loss function for improving the stability of training, and get the most suitable neural network construction for our research. After our optimization, the error decreases from 0.315 m to 0.008 m in cuboid model test. Finally, we apply our method to the cavitation tunnel model. A total of 100 data sets were used for training, 10 sets for verification, and 5 for testing. The average error of the test result is 0.13 m. For the model test in cavitation tunnel in National Taiwan Ocean University, the length of ship model is around 7 m. And the average error is sufficient to determine the noise source position.