Search Results (63)

This study introduces an algorithm for quantifying oceanic wind speed and identifying sound sources in the local underwater soundscape. Utilizing low-complexity metrics like one-minute spectral kurtosis and power spectral density levels, the algorithm categorizes different soundscapes and estimates wind speed. It detects rain, vessels, fin and blue whales, as well as clicks and whistles from dolphins. Positioned as a foundational tool for implementing the Ocean Sound Essential Ocean Variable (EOV), it contributes to understanding long-term trends in climate change for sustainable ocean health and predicting threats through forecasts. The proposed soundscape classification algorithm, validated using extensive acoustic recordings (≥32 kHz) collected at various depths and latitudes, demonstrates high performance, achieving an average precision of $89\%$ and an average recall of $86.59\%$ through optimized parameter tuning via a genetic algorithm. Here, wind speed is determined using a cubic function with power spectral density (PSD) at 6 kHz and the MASLUW method, exhibiting strong agreement with satellite data below 15 m/s. Designed for compatibility with low-power electronics, the algorithm can be applied to both archival datasets and real-time data streams. It provides a straightforward metric for ocean monitoring and sound source identification. Full article

Recent advances in passive acoustic monitoring (PAM) have markedly improved the ability to study marine soundscapes by enabling long-term, non-invasive monitoring of biological sounds across large spatial and temporal scales. Among aquatic organisms, fish are primary contributors to biophony, producing sounds associated with feeding, reproduction, and social behavior. However, the majority of previous research has focused on individual vocalizations, with limited attention to collective acoustic phenomena such as fish choruses. This study quantitatively analyzes choruses produced by the small yellow croaker (Larimichthys polyactis), an ecologically and commercially important species in the Northwest Pacific Ocean. Using power spectral density (PSD) analysis, we examined long-term underwater recordings from a sea cage containing approximately 2000 adult small yellow croakers. The choruses were centered around ~600 Hz and exhibited sound pressure levels 15–20 dB higher at night than during the day. These findings highlight the ecological relevance of fish choruses and support their potential use as indicators of biological activity. This study lays the foundation for incorporating fish choruses into soundscape-based PAM frameworks to enhance biodiversity and habitat monitoring. Full article

Growing concern over anthropogenic underwater noise, highlighted by initiatives like the Marine Strategy Framework Directive (MSFD) and its Technical Group on Underwater Noise (TG Noise), emphasizes regions like the Western Black Sea, where increasing activities threaten marine habitats. This region is experiencing rapid growth in maritime traffic and resource exploitation, which is intensifying concerns over the noise impacts on its unique marine habitats. While machine learning offers promising solutions, a research gap persists in comprehensively evaluating diverse ML models within an integrated framework for complex underwater acoustic data, particularly concerning real-world data limitations like class imbalance. This paper addresses this by presenting a multi-faceted framework using passive acoustic monitoring (PAM) data from fixed locations (50–100 m depth). Acoustic data are processed using advanced signal processing (broadband Sound Pressure Level (SPL), Power Spectral Density (PSD)) for feature extraction (Mel-spectrograms for deep learning; PSD statistical moments for classical/unsupervised ML). The framework evaluates Convolutional Neural Networks (CNNs), Random Forest, and Support Vector Machines (SVMs) for noise event classification, alongside Gaussian Mixture Models (GMMs) for anomaly detection. Our results demonstrate that the CNN achieved the highest classification accuracy of 0.9359, significantly outperforming Random Forest (0.8494) and SVM (0.8397) on the test dataset. These findings emphasize the capability of deep learning in automatically extracting discriminative features, highlighting its potential for enhanced automated underwater acoustic monitoring. Full article

Anthropogenic underwater noise is an increasing form of pollution that negatively affects biota. The effect of this pollutant on many marine species is still largely unknown, especially those that are more sensitive to particle motion than to sound pressure. In these cases, experiments at sea are necessary, due to the difficulty of recreating the particle movement of a real acoustic field under laboratory conditions. This work aims to contribute to the knowledge of the effect of ship noise on the behaviour of mussels (Mytilus galloprovincialis), performing measurements at sea on a real mussel cultivation raft for the first time. The study is carried out on cluster-forming individuals living in the rafts where they are cultivated. Their behaviour is monitored by means of valvometry systems, which measure the magnitude of shell opening using a High-Frequency Non-Invasive (HFNI) system. Simultaneously, the acoustic field generated by the abundant traffic in the area is measured. The results show cause-and-effect relationships between ship noise and valve closure events. Full article

Accurate acoustic identification of marine mammals is vital for monitoring ocean health and human impacts. Existing methods often struggle with limited single-feature representations or suboptimal fusion of multiple features. This paper proposes an Evaluation-Adaptive Weighted Multi-Head Fusion Network that integrates CQT and STFT features via a dual-branch ResNet architecture. The model enhances intra-branch features using channel attention and adaptive weighting of each branch based on its validation accuracy during training. Experiments on the Watkins Marine Mammal Sound Database show that the proposed method achieves superior performance, reaching 96.05% accuracy and outperforming baseline and attention-based fusion models. This approach offers an effective solution for multi-feature acoustic recognition in complex underwater environments. Full article

Monitoring dolphins in the open sea is essential for understanding their behavior and the impact of human activities on the marine ecosystems. Passive Acoustic Monitoring (PAM) is a non-invasive technique for tracking dolphins, providing continuous data. This study presents a novel approach for classifying dolphin vocalizations from a PAM acoustic recording using a convolutional neural network (CNN). Four types of common bottlenose dolphin (Tursiops truncatus) vocalizations were identified from underwater recordings: whistles, echolocation clicks, burst pulse sounds, and feeding buzzes. To enhance classification performances, edge-detection filters were applied to spectrograms, with the aim of removing unwanted noise components. A dataset of nearly 10,000 spectrograms was used to train and test the CNN through a 10-fold cross-validation procedure. The results showed that the CNN achieved an average accuracy of 95.2% and an F1-score of 87.8%. The class-specific results showed a high accuracy for whistles (97.9%), followed by echolocation clicks (94.5%), feeding buzzes (94.0%), and burst pulse sounds (92.3%). The highest F1-score was obtained for whistles, exceeding 95%, while the other three vocalization typologies maintained an F1-score above 80%. This method provides a promising step toward improving the passive acoustic monitoring of dolphins, contributing to both species conservation and the mitigation of conflicts with fisheries. Full article

Hydrophones used in Passive Acoustic Monitoring generate vast amounts of data, with the storage requirements for raw signals dependent on the sampling frequency, which limits the range of frequencies that can be recorded. Since the installation of these observatories is costly, it is crucial to maximize the utility of high-sampling-rate recordings to expand the range of survey types. However, storing these large datasets for long-term trend analysis presents significant challenges. This paper proposes an approach that reduces the data storage requirements by up to 85% while preserving critical information about Power Spectral Density and Sound Pressure Level. The strategy involves generating these key metrics from spectrograms, enabling both short-term (micro) and long-term (macro) studies. A proposal for efficient data processing is presented, structured in three steps: the first focuses on generating key metrics to replace space-consuming raw signals, the second addresses the treatment of these metrics for long-term studies, and the third outlines the creation of event detectors from the processed metrics. A comprehensive overview of the essential features for analyzing acoustic signals is provided, along with considerations for the future design of marine observatories. The necessary calculations and processes are detailed, demonstrating the potential of these methods to address the current data storage and processing limitations in long-term acoustic monitoring. Full article

The accurate acquisition of underwater topographic data is crucial for the representation of river morphology and early warning of water hazards. Owing to the complexity of the underwater environment, there are inevitably outliers in monitoring data, which objectively reduce the accuracy of the data; therefore, anomalous data detection and processing are key in effectively using data. To address anomaly detection in underwater terrain data, this paper presents an optimised DBSCAN-IForest algorithm model, which adopts a distributed computation strategy. First, the K-distance graph and Kd-tree methods are combined to determine the key computational parameters of the DBSCAN algorithm, and the DBSCAN algorithm is applied to perform preliminary cluster screening of underwater terrain data. The isolated forest algorithm is subsequently used to carry out refined secondary detection of outliers in multiple subclusters that were initially screened. Finally, the algorithm performance is verified through example calculations using a dataset of about 8500 underwater topographic points collected from the Yellow River Basin, which includes both elevation and spatial distribution attributes; the results show that compared with other methods, the algorithm has greater efficiency in outlier detection, with a detection rate of up to 93.75%, and the parameter settings are more scientifically sound and reasonable. This research provides a promising framework for anomaly detection in underwater terrain data. Full article

Multibeam echosounder (MBES) backscatter data are influenced by underwater sound absorption, which is dependent on environmental parameters such as temperature, salinity, and depth. This study leverages CTD datasets from the Korea Oceanographic Data Center (KODC) to analyze and visualize the spatiotemporal variations in absorption parameters in the East Sea of Korea, which are subject to pronounced variability over time and space. The legacy MBES backscatter data, originally processed using generalized absorption parameters that neglected spatiotemporal variations, were compared with the calibrated data. The calibration process included inverse calculation of water temperature with depth-specific average salinity values from the nearest KODC stations. This calibration revealed discrepancies of up to 2.1 dB in backscatter intensity across survey lines, highlighting the potential misrepresentation of legacy MBES backscatter data due to site-specific absorption variability having been overlooked. By addressing these discrepancies, this study underscores the importance of incorporating spatiotemporal absorption variability into MBES calibration workflows. This integrated approach not only enhances the reliability of legacy MBES data but also provides valuable insights for marine resource management, seafloor mapping, and environmental monitoring in highly dynamic marine environments such as the East Sea of Korea. Full article

Three-dimensional hydro-acoustic imaging is a research hot spot in the underwater acoustic signal processing field, which has a wide range of application prospects in marine environmental resource surveying, seabed topography and geomorphological mapping, and underwater early warning and monitoring. To solve the problem that the resolution of the current imaging sonar reduces rapidly with increase in distance and a scanning gap exists in side-scan sonar, we designed a down-looking 3D-imaging sonar with a linear array structure. The imaging scheme adopts a time-domain spatial beam-forming method with the Back Projection (BP) algorithm as the core, and the formation of a virtual plane array can effectively improve the along-track resolution. To cope with the interference of the carrier motion error on the imaging, we proposed a high-precision sub-wavelength motion compensation method based on a real-time acoustic calibration system. Simulation and real data experiments show that the motion compensation method can effectively eliminate the influence of motion error and make the imaging energy more focused, leading to higher-quality acoustic images. Under equal average energy, the maximum superimposed sound intensity values in the imaging results increased by 20.75 dB and 6.57 dB, respectively, for simulation and real data. After motion compensation, the resolution of this imaging system reached 3 cm × 3 cm × 2.5 cm @ Depth = 17 m, TBP = 30 s · Hz. Full article

With the rapid advancement of underwater communication and unmanned aerial vehicle (UAV) technologies, the potential applications of cross-medium communication in environmental monitoring, maritime Internet of Things (IoTs), and rescue operations, in particular, have attracted great attention. This study explores the feasibility of achieving cross-medium direct acoustic communication through the air–water interface. Specifically, it investigates challenges such as acoustic impedance mismatches and signal attenuation caused by energy loss during interface transmission, aiming to understand their impact on communication performance. Experimental tests employed underwater acoustic transducers as signal transmitters to propagate sound waves directly into the air, attempting to establish communication links with aerial UAV nodes. Preliminary experimental results indicate that even conventional underwater acoustic transducers can achieve information exchange between underwater nodes and UAVs, laying a foundation for further research and application of cross-medium communication systems. Full article

Since the Industrial Revolution, underwater soundscapes have become more complex and contaminated due to increased cumulative human activities. Anthropogenic underwater sources have been growing in number, and shipping noise has become the primary source of chronic acoustic exposure. However, global data on current and historic noise levels is lacking. Here, using the Listening to the Deep-Ocean Environment network, we investigated the baseline shipping noise levels in thirteen observatories (eight stations from ONC Canada, four from the JAMSTEC network, and OBSEA in the Mediterranean Sea) and, in five of them, animal presence. Our main results show yearly noise variability in the studied locations that is not dominated by marine traffic but by natural and biological patterns. The halt in transportation due to COVID was insignificant when the data were recorded far from shipping routes. In order to better design a legislative framework for mitigating noise impacts, we highlight the importance of using tools that allow for long-term acoustic monitoring, automated detection of sounds, and big data handling and management. Full article

Whale sound is a typical transient signal. The escalating demands of ecological research and marine conservation necessitate advanced technologies for the automatic detection and classification of underwater acoustic signals. Traditional energy detection methods, which focus primarily on amplitude, often perform poorly in the non-Gaussian noise conditions typical of oceanic environments. This study introduces a classified-before-detect approach that overcomes the limitations of amplitude-focused techniques. We also address the challenges posed by deep learning models, such as high data labeling costs and extensive computational requirements. By extracting shape statistical features from audio and using the XGBoost classifier, our method not only outperforms the traditional convolutional neural network (CNN) method in accuracy but also reduces the dependence on labeled data, thus improving the detection efficiency. The integration of these features significantly enhances model performance, promoting the broader application of marine acoustic remote sensing technologies. This research contributes to the advancement of marine bioacoustic monitoring, offering a reliable, rapid, and training-efficient method suitable for practical deployment. Full article

Vibration sensors are widely applied in the detection of faults and analysis of operational states in engineering machinery and equipment. However, commercial vibration sensors with a feature of high hardness hinder their usage in some practical applications where the measured objects have irregular surfaces that are difficult to install. Moreover, as the operating environments of machinery become increasingly complex, there is a growing demand for sensors capable of working in wet and humid conditions. Here, we present a flexible, superhydrophobic vibration sensor with parallel microcracks. The sensor is fabricated using a femtosecond laser direct writing ablation strategy to create the parallel cracks on a PDMS film, followed by spray-coating with a conductive ink composed of MWCNTs, CB, and PDMS. The results demonstrate that the developed flexible sensor exhibits a high-frequency response of up to 2000 Hz, a high acceleration response of up to 100 m/s², a water contact angle as high as 159.61°, and a linearity of 0.9812 between the voltage signal and acceleration. The results indicate that the sensor can be employed for underwater vibration, sound recognition, and vibration monitoring in fields such as shield cutters, holding significant potential for mechanical equipment vibration monitoring and speech-based human–machine interaction. Full article

In the shift toward sustainable energy production, offshore wind power has experienced notable expansion. Several projects to install floating offshore wind farms in European waters, ranging from a few to hundreds of turbines, are currently in the planning stage. The underwater operational sound generated by these floating turbines has the potential to affect marine ecosystems, although the extent of this impact remains underexplored. This study models the sound radiated by three planned floating wind farms in the Strait of Sicily (Italy), an area of significant interest for such developments. These wind farms vary in size (from 250 MW to 2800 MW) and environmental characteristics, including bathymetry and seabed substrates. Propagation losses were modeled in one-third-octave bands using JASCO Applied Sciences’ Marine Operations Noise Model, which is based on the parabolic equation method, combined with the BELLHOP beam-tracing model. Two sound speed profiles, corresponding to winter and summer, were applied to simulate seasonal variations in sound propagation. Additionally, sound from an offshore supply ship was incorporated with one of these wind farms to simulate maintenance operations. Results indicate that sound from operating wind farms could reach a broadband sound pressure level (L_p) of 100 dB re 1 µPa as far as 67 km from the wind farm. Nevertheless, this sound level is generally lower than the ambient sound in areas with intense shipping traffic. The findings are discussed in relation to local background sound levels and current guidelines and regulations. The implications for environmental management include the need for comprehensive monitoring and mitigation strategies to protect marine ecosystems from potential acoustic disturbances. Full article