A time-delay neural network for ship-radiated noise recognition based on residual block and attention mechanism

Ship-radiated noise recognition remains a challenging issue in underwater acoustic signal processing for a variety of reasons. The ocean ambient noise influences the signal quality, and the scarcity of samples limits the performance of deep learning algorithms. In this study, a self-attention mechanism and residual block-based time-delay neural network (SRTDNN) is proposed to improve the recognition performance of ship-radiated noise. The noise samples are first segmented and transformed into three-dimensional Mel-scale frequency cepstral coefficients (3D-MFCC), which combine static and dynamic characteristics of targets through the concatenation of MFCC, Delta MFCC and Delta-Delta MFCC. Subsequently, 3D-MFCC are fed as input to the SRTDNN. The SRTDNN extracts time-domain features of inputs through the TDNN layers and then assigns different weights to the frame and channel dimensions of the feature maps through two attention mechanisms. The Res Connections and Res2Block are interspersed to facilitate convergence. During training, a time-domain and frequency-domain data augmentation pipeline is designed to alleviate the lack of samples. Experiments with public and real sea experiment datasets are conducted to compare the SRTDNN with acknowledged high-quality recognition methods. The results show that the SRTDNN has a significant improvement in recognition performance. Furthermore, the ablation tests demonstrate the effectiveness of the proposed method.