The core of underwater acoustic recognition is to extract the spectral features of targets. The running speed and track of the targets usually result in a Doppler shift, which poses significant challenges for recognizing targets with different Doppler frequencies. This paper proposes deep learning with a channel attention mechanism approach for underwater acoustic recognition. It is based on three crucial designs. Feature structures can obtain high-dimensional underwater acoustic data. The feature extraction model is the most important. First, we develop a ResNet to extract the deep abstraction spectral features of the targets. Then, the channel attention mechanism is introduced in the camResNet to enhance the energy of stable spectral features of residual convolution. This is conducive to subtly represent the inherent characteristics of the targets. Moreover, a feature classification approach based on one-dimensional convolution is applied to recognize targets. We evaluate our approach on challenging data containing four kinds of underwater acoustic targets with different working conditions. Our experiments show that the proposed approach achieves the best recognition accuracy (98.2%) compared with the other approaches. Moreover, the proposed approach is better than the ResNet with a widely used channel attention mechanism for data with different working conditions.

Long-range underwater targets must be accurately and quickly identified for both defense and civil purposes. However, the performance of an underwater acoustic target recognition (UATR) system can be significantly affected by factors such as lack of data and ship working conditions. As the marine environment is very complex, UATR relies heavily on feature engineering, and manually extracted features are occasionally ineffective in the statistical model. In this paper, an end-to-end model of UATR based on a convolutional neural network and attention mechanism is proposed. Using raw time domain data as input, the network model combines residual neural networks and densely connected convolutional neural networks to take full advantage of both. Based on this, a channel attention mechanism and a temporal attention mechanism are added to extract the information in the channel dimension and the temporal dimension. After testing the measured four types of ship-radiated noise dataset in experiments, the results show that the proposed method achieves the highest correct recognition rate of 97.69% under different working conditions and outperforms other deep learning methods.

Recently, deep learning has been widely used in ship-radiated noise classification. To improve classification efficiency, avoiding high computational costs is an important research direction in ship-radiated noise classification. We propose a lightweight squeeze and excitation residual network 10 (LW-SEResNet10). In ablation experiments of LW-SEResNet10, the use of ResNet10 instead of ResNet18 reduced 56.1% of parameters, while the accuracy is equivalent to ResNet18. The improved accuracy indicates that the ReLU6 enhanced the model stability, and an attention mechanism captured the channel dependence. The ReLU6 activation function does not introduce additional parameters, and the number of parameters introduced by the attention mechanism accounts for 0.2‰ of the model parameters. The 3D dynamic MFCC feature performs better than MFCC, Mel-spectrogram, 3D dynamic Mel-spectrogram, and CQT. Moreover, the LW-SEResNet10 model is also compared with ResNet and two classic lightweight models. The experimental results show that the proposed model achieves higher classification accuracy and is lightweight in terms of not only the model parameters, but also the time consumption. LW-SEResNet10 also outperforms the state-of-the-art model CRNN-9 by 3.1% and ResNet by 3.4% and has the same accuracy as AudioSet pretrained STM, which achieves the trade-off between accuracy and model efficiency.

Ship-radiated noise separation is critical in both military and economic domains. However, due to the complex underwater environments with multiple noise sources and reverberation, separating ship-radiated noise poses a significant challenge. Traditionally, underwater acoustic signal separation has employed blind source separation methods based on independent component analysis. Recently, the separation of underwater acoustic signals has been approached as a deep learning problem. This involves learning the features of ship-radiated noise from training data. This paper introduces a deep time-domain network for ship-radiated noise separation by leveraging the power of parallel dilated convolution and group convolution. The separation layer employs parallel dilated convolution operations with varying expansion factors to better extract low-frequency features from the signal envelope while preserving detailed information. Additionally, we use group convolution to reduce the expansion of network size caused by parallel convolution operations, enabling the network to maintain a smaller size and computational complexity while achieving good separation performance. The proposed approach is demonstrated to be superior to the other common networks in the DeepShip dataset through comprehensive comparisons.

We report a kind of coiled-up meta-liners with nonuniform cross sections (CMNC), which can efficiently attenuate low-frequency sound waves under grazing flow with a deep subwavelength thickness (e.g., ∼λ/17 at 500 Hz). At a grazing flow Mach number of 0.26, the average transmission loss of the meta-liner is 12.6 dB at 500–1000 Hz, which is twice as much as that of a double-degree-of-freedom acoustic liner of the same size. Physically, the nonuniform cross-sectional distribution and significant cross-sectional area ratio enhances vortex shedding, thus resulting in severe acoustic energy dissipation. The excellent low-frequency acoustic attenuation performance of CMNC is investigated thoroughly with experimental, theoretical, and numerical methods. This work provides an avenue for low-frequency noise reduction in grazing flow scenarios (e.g., in a high bypass ratio turbofan engine).