Underwater acoustic target recognition based on convolutional neural network and multi-feature fusion

Jiabao Tan; Xiang Pan

doi:10.1117/12.2684510

1 August 2023 Underwater acoustic target recognition based on convolutional neural network and multi-feature fusion

Jiabao Tan, Xiang Pan

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 1275432 (2023) https://doi.org/10.1117/12.2684510
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

Underwater acoustic target recognition has been faced with significant challenges due to the noise in the ocean environment and the complex and ever-changing nature of ocean channels. this paper proposes an underwater acoustic target recognition method based on a convolutional neural network and multi-feature fusion. Various features including the Amplitude Modulation Spectrogram, Mel Frequency Cepstral Coefficient, Relative Spectral Transform-Perceptual Linear Prediction, Gammatone Frequency Cepstral Coefficient and Delta feature of underwater acoustic targets are effectively extracted and then fused to form AMCG-Delta features. To address the issue of data scarcity, data augmentation techniques including pitch shifting, time stretching, random addition of noise and SpecAugment are used. Finally, the Ecapa-OLS network is proposed to improve the accuracy of underwater acoustic target recognition. With the shipsEar dataset, the proposed method achieves a recognition accuracy that is 6.95% higher than that of the baseline method.

Citation Download Citation

Jiabao Tan and Xiang Pan "Underwater acoustic target recognition based on convolutional neural network and multi-feature fusion", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 1275432 (1 August 2023); https://doi.org/10.1117/12.2684510

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KEYWORDS

Acoustics

Target recognition

Convolutional neural networks

Feature extraction

Time-frequency analysis

Visualization

Feature fusion

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