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

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.