A Deep Learning Method for Ship-Radiated Noise Recognition Based on MFCC Feature

Underwater target recognition is one of the most challenging tasks in underwater signal processing. Previous deep learning methods have relied on fusing more acoustic features, ignoring the rich information contained in the time-frequency features of underwater acoustics. Furthermore, fusing features that are less relevant to the target may result in redundancy and affect the recognition performance of the model. In this paper, a novel method based on Multi-Scale feature extraction, Attention mechanism for feature fusion and Convolutional Recurrent Neural Network (MACRN) is proposed to fully exploit the deep features of Mel-Frequency Cepstral Coefficients (MFCC) for underwater target recognition. In comparative experiments conducted on the open-source dataset ShipsEar, the proposed method achieves an average recognition accuracy of 98.1%