Underwater Acoustic Target Recognition with a Residual Network and the Optimized Feature Extraction Method

Hong, Feng; Liu, Chengwei; Guo, Lijuan; Chen, Feng; Feng, Haihong

doi:10.3390/app11041442

Open AccessArticle

Underwater Acoustic Target Recognition with a Residual Network and the Optimized Feature Extraction Method

by

Feng Hong

^1,*

,

Chengwei Liu

^1,2,

Lijuan Guo

³,

Feng Chen

¹ and

Haihong Feng

¹

Shanghai Acoustics Laboratory, Chinese Academy of Sciences, Shanghai 201805, China

²

University of Chinese Academy of Sciences, Beijing 100049, China

³

The School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 200237, China

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2021, 11(4), 1442; https://doi.org/10.3390/app11041442

Submission received: 8 January 2021 / Revised: 29 January 2021 / Accepted: 30 January 2021 / Published: 5 February 2021

(This article belongs to the Section Acoustics and Vibrations)

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Abstract

Underwater Acoustic Target Recognition (UATR) remains one of the most challenging tasks in underwater signal processing due to the lack of labeled data acquisition, the impact of the time-space varying intrinsic characteristics, and the interference from other noise sources. Although some deep learning methods have been proven to achieve state-of-the-art accuracy, the accuracy of the recognition task can be improved by designing a Residual Network and optimizing feature extraction. To give a more comprehensive representation of the underwater acoustic signal, we first propose the three-dimensional fusion features along with the data augment strategy of SpecAugment. Afterward, an 18-layer Residual Network (ResNet18), which contains the center loss function with the embedding layer, is designed to train the aggregated features with an adaptable learning rate. The recognition experiments are conducted on the ship-radiated noise dataset from a real environment, and the accuracy results of 94.3% indicate that the proposed method is appropriate for underwater acoustic recognition problems and sufficiently surpasses other classification methods.

Keywords: ResNet; underwater acoustics; ShipsEar; embedding; SpecAugment; UATR; MFCC; combined features

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MDPI and ACS Style

Hong, F.; Liu, C.; Guo, L.; Chen, F.; Feng, H. Underwater Acoustic Target Recognition with a Residual Network and the Optimized Feature Extraction Method. Appl. Sci. 2021, 11, 1442. https://doi.org/10.3390/app11041442

AMA Style

Hong F, Liu C, Guo L, Chen F, Feng H. Underwater Acoustic Target Recognition with a Residual Network and the Optimized Feature Extraction Method. Applied Sciences. 2021; 11(4):1442. https://doi.org/10.3390/app11041442

Chicago/Turabian Style

Hong, Feng, Chengwei Liu, Lijuan Guo, Feng Chen, and Haihong Feng. 2021. "Underwater Acoustic Target Recognition with a Residual Network and the Optimized Feature Extraction Method" Applied Sciences 11, no. 4: 1442. https://doi.org/10.3390/app11041442

APA Style

Hong, F., Liu, C., Guo, L., Chen, F., & Feng, H. (2021). Underwater Acoustic Target Recognition with a Residual Network and the Optimized Feature Extraction Method. Applied Sciences, 11(4), 1442. https://doi.org/10.3390/app11041442

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Underwater Acoustic Target Recognition with a Residual Network and the Optimized Feature Extraction Method

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