Search Results (102)

Ship-radiated noise (SRN) recognition is vital for underwater acoustics, with applications in both military and civilian fields. Traditional manual recognition by sonar operators is inefficient and error-prone, motivating the development of automated recognition systems. However, most existing deep learning approaches demand high computational resources, limiting their deployment on resource-constrained edge devices. To overcome this challenge, we propose LW-MS-LFTFNet, a lightweight model informed by time-frequency analysis of SRN that highlights the critical role of low-frequency components. The network integrates a multi-scale depthwise separable convolutional backbone with CBAM attention for efficient spectral representation, along with two LSTM-based modules to capture temporal dependencies in low-frequency bands. Experiments on the DeepShip dataset show that LW-MS-LFTFNet achieves 75.04% accuracy with only 0.85 M parameters, 0.38 GMACs, and 3.27 MB of storage, outperforming representative lightweight architectures. Ablation studies further confirm that low-frequency temporal modules contribute complementary gains, improving accuracy by 2.64% with minimal overhead. Guided by domain-specific priors derived from time-frequency pattern analysis, LW-MS-LFTFNet achieves efficient and accurate SRN recognition with strong potential for edge deployment. Full article

Shipping expansion, offshore energy generation, fish farming, and construction work radiate high levels of underwater noise, which may critically stress marine ecosystems. Tools for simulating, analyzing, and forecasting underwater noise can be of great help in understanding the impact of underwater radiated noise both on the environment and on man-made equipment, such as underwater communication and telemetry systems. To address this challenge, we developed a web-based Marine Noise Analysis Tool (MNAT) that models, simulates, and predicts underwater radiated noise levels. To reproduce realistic shipping conditions, MNAT combines real-time Automatic Identification System data with environmental data using broadly accepted underwater acoustic propagation models, including Bellhop and RAM. Moreover, MNAT can simulate other kinds of noise sources, such as seismic airguns. It features an intuitive interface enabling real-time tracking, noise impact assessment, and interactive visualizations. MNAT’s noise modeling capabilities allow the user to design resilient communication systems in different noise conditions, analyze maritime noise data, and forecast future noise levels, with potential contributions to the design of noise-resilient systems, to the optimization of environmental monitoring device deployments, and to noise mitigation policymaking. MNAT has been made available for the community at a public GIT repository. Full article

Scientific awareness is rising regarding fish and sea invertebrates’ sensitivity to the sound field’s particle motion component. The AquaVib, a distinctive laboratory setup, provides a practical methodology for controlled sound exposure experiments on small aquatic organisms, enabling a separate assessment of their acoustic pressure- and particle motion-elicited responses across a range of realistic scenarios. The chosen facility design permits the reproduction of realistic sound exposures at different kinetic-to-potential energy ratios, with characteristics similar to underwater-radiated noise from human activities such as shipping or offshore installations (<1 kHz). It provides a cost-efficient multimodal approach to investigate potential physiological, pathological, and ultrastructural effects on small aquatic organisms at any stage of maturity. This study details the vibroacoustic characterization of the AquaVib system, identifies key challenges, and outlines planned improvements. The ultimate goal of the presented approach is to contribute to the scientific community and competent authorities in covering the main gaps in current knowledge on the sensitivity of aquatic organisms to the particle motion component and to identify and quantify potential acute and long-term detrimental effects arising from human activities. Full article

Accurate identification of mechanical excitation forces is of great significance for the control of ship radiated noise and structural design. Currently, the identification of excitation forces mostly relies on indirect calculations, which suffer from ill-conditioned problems. Regularization correction is one of the main means to solve this problem. Although regularization methods have been widely developed, their application in the field of ships is relatively rare. Currently, the commonly used methods are truncation singular values and Givonov regularization methods. This paper starts from the practical application of ships and addresses the problem of poor correction effect of traditional regularization methods. Two optimized regularization methods, quasi-optimal discriminant criterion and B-spline interpolation function method, are proposed. These methods are verified through simulations and experiments. The results of the scaled model experiments show that compared with using the L-curve alone, the Q-O method reduces the regularization error by 29%, while the BL curve improves the robustness by 38% under a 15 dB noise condition. Full article

The scarcity of underwater acoustic data in deep and remote sea environments poses a significant challenge to data-driven target recognition models, severely restricting their performance. To address this challenge, this study presents a ray-theory-based data augmentation method for generating synthetic ship-radiated noise datasets in oceanic environments at a depth of 3500 m—DS3500, encompassing both direct and shadow zones. Additionally, a novel MEG (multi-task, multi-expert, multi-gate) framework is developed to achieve simultaneous target localization and recognition by integrating relative positional information between the target and sonar, which dynamically partitions parameter spaces through multi-expert mechanisms and adaptively combines task-specific representations using multi-gate attention to simultaneously predict target localization and recognition. Experimental results on the DS3500 dataset demonstrate that the MEG framework achieves 95.93% recognition accuracy, a range localization error of 0.2011 km and a depth localization error of 20.61 m with a maximum detection range of 11 km and depth of 1100 m. This study provides a new technical solution for underwater acoustic target recognition in deep and remote seas, offering innovative approaches for practical applications in marine monitoring and defense. Full article

Underwater sonar target recognition is crucial in fields such as national defense, navigation, and environmental monitoring. However, it faces issues such as the complex characteristics of ship-radiated noise, imbalanced data distribution, non-stationarity, and bottlenecks of existing technologies. This paper proposes the MultiFuseNet-AID network, aiming to address these challenges. The network includes the TriFusion block module, the novel lightweight attention residual network (NLARN), the long- and short-term attention (LSTA) module, and the Mamba module. Through the TriFusion block module, the original, differential, and cumulative signals are processed in parallel, and features such as MFCC, CQT, and Fbank are fused to achieve deep multi-domain feature fusion, thereby enhancing the signal representation ability. The NLARN was optimized based on the ResNet architecture, with the SE attention mechanism embedded. Combined with the long- and short-term attention (LSTA) and the Mamba module, it could capture long-sequence dependencies with an O(N) complexity, completing the optimization of lightweight long sequence modeling. At the same time, with the help of feature fusion, and layer normalization and residual connections of the Mamba module, the adaptability of the model in complex scenarios with imbalanced data and strong noise was enhanced. On the DeepShip and ShipsEar datasets, the recognition rates of this model reached 98.39% and 99.77%, respectively. The number of parameters and the number of floating point operations were significantly lower than those of classical models, and it showed good stability and generalization ability under different sample label ratios. The research shows that the MultiFuseNet-AID network effectively broke through the bottlenecks of existing technologies. However, there is still room for improvement in terms of adaptability to extreme underwater environments, training efficiency, and adaptability to ultra-small devices. It provides a new direction for the development of underwater sonar target recognition technology. Full article

Accurate prediction of ship underwater radiated noise (URN) during navigation is critical for evaluating acoustic stealth performance and analyzing detection risks. However, the labeled data available for the training of URN prediction model is limited. Semi-supervised learning (SSL) can improve the model performance by using unlabeled data in the case of a lack of labeled data. Therefore, this paper proposes an SSL method for URN prediction. First, an anti-perturbation regularization is constructed using unlabeled data to optimize the objective function of EL, which is then used in the Genetic algorithm to adaptively optimize base learner weights, to enhance pseudo-label quality. Second, a semi-supervised ensemble (ESS) framework integrating dynamic pseudo-label screening and uncertainty bias correction (UBC) is established, which can dynamically select pseudo-labels based on local prediction performance improvement and reduce the influence of pseudo-labels’ uncertainty on the model. Experimental results of the cabin model and sea trials of the ship demonstrate that the proposed method reduces prediction errors by up to 65.5% and 62.1% compared to baseline supervised and semi-supervised regression models, significantly improving prediction accuracy. Full article

Underwater radiated noise power estimation is crucial for the quantitative assessment of noise levels emitted by ships and underwater vehicles. This paper therefore proposes a maximum likelihood estimation method for determining the power of underwater radiated noise. The method establishes the probability density function of the hydrophones array received data and derives the minimum variance unbiased estimation of the power through theoretical analysis under the maximum likelihood criterion. Numerical simulations and experimental data demonstrate that this method can significantly reduce the influence of ambient noise on estimation results and improve the estimation accuracy under low signal-to-noise ratio conditions, outperforming commonly used beamforming-based estimation methods. In addition, the estimation variance achieves the Cramér–Rao lower bound, which is consistent with theoretical derivation. When the source position is unknown, this method can simultaneously localize the sound source and estimate its power by searching for the maximum value within a specified region. Full article

Marine ship-radiated noise and multipath Doppler effect reduce the positioning accuracy of linear frequency modulation (LFM) signals in ocean waveguide environments. However, the assumption of Gaussian noise underlying most time–frequency domain algorithms limits their effectiveness in mitigating non-Gaussian interference. To address this issue, we propose a Deep-separable Conformer Wave-Unet (DC-WUnet)-based underwater acoustic signal enhancement network designed to reconstruct signals from interference and noise. The encoder incorporates the Conformer module and skip connections to enhance the network’s multiscale feature extraction capability. Meanwhile, the network introduces depthwise separable convolution to reduce the number of parameters and improve computational efficiency. The decoder applies a slope-based linear interpolation method for upsampling to avoid introducing high-frequency noise during decoding. Additionally, the loss function employs joint time–frequency domain constraints to prevent signal loss and compression, particularly under low Signal-to-Noise Ratio (SNR) conditions. Experimental evaluations under an SNR of −10 dB indicate that the proposed method achieves at least a 32% improvement in delay estimation accuracy and a 2.3 dB enhancement in output SNR relative to state-of-the-art baseline algorithms. Consistent performance advantages are also observed under varying SNR conditions, thereby validating the effectiveness of the proposed approach in shipborne noisy environments. Full article

In order to improve the efficiency of the deep network model in processing the radiated noise signals of underwater acoustic targets, this paper introduces a Singular Spectrum Analysis and Channel Attention Convolutional Neural Network (SSA-CACNN) model. The front end of the model is designed as an SSA filter, and its input is the time-domain signal that has undergone simple preprocessing. The SSA method is utilized to separate the noise efficiently and reliably from useful signals. The first three orders of useful signals are then fed into the CACNN model, which has a convolutional layer set up at the beginning of the model to further remove noise from the signal. Then, the attention of the model to the feature signal channels is enhanced through the combination of multiple groups of convolutional operations and the channel attention mechanism, which facilitates the model’s ability to discern the essential characteristics of the underwater acoustic signals and improve the target recognition rate. Experimental Results: The signal reconstructed by the first three-order waveforms at the front end of the SSA-CACNN model proposed in this paper can retain most of the features of the target. In the experimental verification using the ShipsEar dataset, the model achieved a recognition accuracy of 98.64%. The model’s parameter count of 0.26 M was notably lower than that of other comparable deep models, indicating a more efficient use of resources. Additionally, the SSA-CACNN model had a certain degree of robustness to noise, with a correct recognition rate of 84.61% maintained when the signal-to-noise ratio (SNR) was −10 dB. Finally, the pre-trained SSA-CACNN model on the ShipsEar dataset was transferred to the DeepShip dataset with a recognition accuracy of 94.98%. Full article

Underwater acoustic signals typically exhibit non-Gaussian, non-stationary, and nonlinear characteristics. When processing real-world underwater acoustic signals, traditional multivariate entropy algorithms often struggle to simultaneously ensure stability and extract cross-channel information. To address these issues, the improved multivariate multiscale sample entropy (IMMSE) algorithm is proposed, which extracts the complexity of multi-channel data, enabling a more comprehensive and stable representation of the dynamic characteristics of complex nonlinear systems. This paper explores the optimal parameter selection range for the IMMSE algorithm and compares its sensitivity to noise and computational efficiency with traditional multivariate entropy algorithms. The results demonstrate that IMMSE outperforms its counterparts in terms of both stability and computational efficiency. Analysis of various types of ship-radiated noise further demonstrates IMMSE’s superior stability in handling complex underwater acoustic signals. Moreover, IMMSE’s ability to extract features enables more accurate discrimination between different signal types. Finally, the paper presents data processing results in mechanical fault diagnosis, underscoring the broad applicability of IMMSE. Full article

Many studies have used various time-frequency feature extraction methods to convert ship-radiated noise into three-dimensional (3D) data suitable for computer vision (CV) models, which have shown good results in public datasets. However, traditional feature engineering (FE) has been enhanced to interface matching–feature engineering (IM-FE). This approach requires considerable effort in feature design, larger sample duration, or a higher upper limit of frequency. In this context, this paper proposes a one-dimensional network design for underwater acoustic target recognition (UATR-ND1D), only combined with fast Fourier transform (FFT), which can effectively alleviate the problem of IM-FE. This method is abbreviated as FFT-UATR-ND1D. FFT-UATR-ND1D was applied to the design of a one-dimensional network, named ResNet1D. Experiments were conducted on two mainstream datasets, using ResNet1D in 4320 and 360 tests, respectively. The lightweight model ResNet1D_S, with only 0.17 M parameters and 3.4 M floating point operations (FLOPs), achieved average accuracies were 97.2% and 95.20%. The larger model, ResNet1D_B, with 2.1 M parameters and 5.0 M FLOPs, both reached optimal accuracies, 98.81% and 98.42%, respectively. Compared to existing methods, those with similar parameter sizes performed 3–5% worse than the methods proposed in this paper. Additionally, methods achieving similar recognition rates require more parameters of 1 to 2 orders of magnitude and FLOPs. Full article

In the context of a complex marine environment, extracting and recognizing underwater acoustic target features using ship-radiated noise present significant challenges. This paper proposes a novel deep neural network model for underwater target recognition, which integrates 3D Mel frequency cepstral coefficients (3D-MFCC) and 3D Mel features derived from ship audio signals as inputs. The model employs a serial architecture that combines a convolutional neural network (CNN) with a long short-term memory (LSTM) network. It replaces the traditional CNN with a multi-scale depthwise separable convolutional network (MSDC) and incorporates a multi-scale channel attention mechanism (MSCA). The experimental results demonstrate that the average recognition rate of this method reaches 87.52% on the DeepShip dataset and 97.32% on the ShipsEar dataset, indicating a strong classification performance. Full article

Feature selection and fusion in ship radiated noise-based underwater target recognition have remained challenging tasks. This paper proposes a novel feature extraction method based on multi-dimensional feature selection and fusion. Redundant features are filtered through feature visualization techniques. The Sub-regional Feature Enhancement modules (SFE) and Multi-activated Channel Aggregation modules (MCA) within the neural network are utilized to achieve underwater target recognition. Experimental results indicate that our network, named Sub-Regional Channel Aggregation Net (SRCA-Net), utilizing 3-s sound segments for ship radiated noise recognition, surpasses existing models, achieving an accuracy of 78.52% on the public DeepShip dataset. Full article

Due to the complexity of underwater environments, underwater target recognition based on radiated noise has always been challenging. This paper proposes a multi-scale frequency-adaptive network for underwater target recognition. Based on the different distribution densities of Mel filters in the low-frequency band, a three-channel improved Mel energy spectrum feature is designed first. Second, by combining a frequency-adaptive module, an attention mechanism, and a multi-scale fusion module, a multi-scale frequency-adaptive network is proposed to enhance the model’s learning ability. Then, the model training is optimized by introducing a time–frequency mask, a data augmentation strategy involving data confounding, and a focal loss function. Finally, systematic experiments were conducted based on the ShipsEar dataset. The results showed that the recognition accuracy for five categories reached 98.4%, and the accuracy for nine categories in fine-grained recognition was 88.6%. Compared with existing methods, the proposed multi-scale frequency-adaptive network for underwater target recognition has achieved significant performance improvement. Full article