Underwater acoustic signal recognition system with multi-scale hybrid cepstral feature strategy and joint deep network

In this paper, we propose a new underwater acoustic signal recognition system to address the recognition difficulties caused by the susceptibility of signals to complex noise interference in underwater acoustic environments. Specifically, the proposed system includes two stages: feature extraction and recognition. Feature extraction: a multi-scale hybrid cepstral feature strategy is proposed. It uses new singular spectrum decomposition to obtain multi-scale components and then extracts the Mel-frequency cepstral coefficients, inverse Mel-frequency cepstral coefficients, Gammatone frequency cepstral coefficients, and linear prediction cepstral coefficients of each component. After feature enhancement and selection, a novel multi-scale hybrid cepstral feature set is constructed. This feature set realizes the complementarity and enhancement of different cepstral features and effectively solves the problems of single feature expression and data redundancy. Recognition: a new joint deep network model is proposed. It adopts the unique design of one-dimensional convolutional neural network (1DCNN) and bidirectional gated recursive unit (BiGRU), which realizes the mutual complement of spatial information extracted by 1DCNN and dependent information captured by BiGRU and effectively improves the processing ability of the model for complex feature sets. In addition, the Kepler optimization algorithm and self-concern mechanism are introduced into the network, which solves the problem of selecting network parameters and improves the focus ability of the model on key features. By setting up multiple groups of comparison and ablation experiments, the recognition results of underwater acoustic data, including ship-radiated noise signals and marine biological signals, show that the recognition accuracy of the proposed system reaches 96.11 % and 98.67 %, respectively, which is better than all comparison methods. In addition, we further verified that the system still has high robustness under a low signal-to-noise ratio, which provides new ideas for research in the field of underwater acoustic signal recognition.