A Novel Underwater Acoustic Target Recognition Method Based on MFCC and RACNN

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 1; p. 273
• Main Authors: Liu, Dali, Yang, Hongyuan, Hou, Weimin, Wang, Baozhu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2024; MDPI
• Subjects: Accuracy; Acoustics; Algorithms; Analysis; attention mechanisms; Deep learning; Design; Fourier transforms; Marine biology; Methods; MFCC; Neural networks; Noise; Remote sensing; residuals mechanisms; ship-radiated noise; Surveys; Target recognition; Technology application; UATR; Wave propagation; ship-radiated noise; attention mechanisms; MFCC; UATR; residuals mechanisms
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24010273

In ocean remote sensing missions, recognizing an underwater acoustic target is a crucial technology for conducting marine biological surveys, ocean explorations, and other scientific activities that take place in water. The complex acoustic propagation characteristics present significant challenges for the recognition of underwater acoustic targets (UATR). Methods such as extracting the DEMON spectrum of a signal and inputting it into an artificial neural network for recognition, and fusing the multidimensional features of a signal for recognition, have been proposed. However, there is still room for improvement in terms of noise immunity, improved computational performance, and reduced reliance on specialized knowledge. In this article, we propose the Residual Attentional Convolutional Neural Network (RACNN), a convolutional neural network that quickly and accurately recognize the type of ship-radiated noise. This network is capable of extracting internal features of Mel Frequency Cepstral Coefficients (MFCC) of the underwater ship-radiated noise. Experimental results demonstrate that the proposed model achieves an overall accuracy of 99.34% on the ShipsEar dataset, surpassing conventional recognition methods and other deep learning models.