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

作者：Hong Yang, Jinmei Li, Guohui Li, Chao Wang · 发表于：Engineering Applications of Artificial Intelligence · 年份：2025 · DOI：10.1016/j.engappai.2025.111702 · 被引用次数：10 · 研究领域：Underwater Acoustics Research、Blind Source Separation Techniques、Speech and Audio Processing

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...