Search Results (416)

This research presents DuSAFNet, a lightweight deep neural network for fine-grained bird audio classification. DuSAFNet combines dual-path feature fusion, spectral–temporal attention, and a multi-band ArcMarginProduct classifier to enhance inter-class separability and capture both local and global spectro–temporal cues. Unlike single-feature approaches, DuSAFNet captures both local spectral textures and long-range temporal dependencies in Mel-spectrogram inputs and explicitly enhances inter-class separability across low, mid, and high frequency bands. On a curated dataset of 17,653 three-second recordings spanning 18 species, DuSAFNet achieves 96.88% accuracy and a 96.83% F1 score using only 6.77 M parameters and 2.275 GFLOPs. Cross-dataset evaluation on Birdsdata yields 93.74% accuracy, demonstrating robust generalization to new recording conditions. Its lightweight design and high performance make DuSAFNet well-suited for edge-device deployment and real-time alerts for rare or threatened species. This work lays the foundation for scalable, automated acoustic monitoring to inform biodiversity assessments and conservation planning. Full article

This research introduces an innovative passive vibration control methodology employing acoustic black hole (ABH) structures to mitigate vibration transmission in electric automotive steering machines—a prevalent issue adversely affecting driving comfort and vehicle safety. Leveraging the inherent bending wave manipulation properties of ABH configurations, we conceive an integrated vibration suppression framework synergizing advanced computational modeling with intelligent optimization algorithms. A high-fidelity finite element (FEM) model integrating ABH-attached steering machine system was developed and subjected to experimental validation via rigorous modal testing. To address computational challenges in design optimization, a hybrid modeling strategy integrating parametric design (using Latin Hypercube Sampling, LHS) with Kriging surrogate modeling is proposed. Systematic parameterization of ABH geometry and damping layer dimensions generated 40 training datasets and 12 validation datasets. Surrogate model verification confirms the model’s precise mapping of vibration characteristics across the design space. Subsequent multi-objective genetic algorithm optimization targeting RMS velocity suppression achieved substantial vibration attenuation (29.2%) compared to baseline parameters. The developed methodology provides automotive researchers and engineers with an efficient suitable design tool for vibration-sensitive automotive component design. Full article

Background: Boiling histotripsy (BH) uses high-amplitude, short-pulse focused ultrasound to disrupt tissue mechanically. Oncolytic virotherapy using reovirus has shown modest clinical benefit in pancreatic cancer patients. Here, reovirus and BH were used to treat pancreatic tumours, and their effects on the immune transcriptome of these tumours were characterised. Methods: Orthotopic syngeneic murine pancreatic KPC tumours grown in immune-competent subjects, were allocated to control, reovirus, BH and combined BH and reovirus treatment groups. Acoustic cavitation was monitored using a passive broadband cavitation sensor. Treatment effects were assessed histologically with hematoxylin and eosin staining. Single-cell multi-omics combining whole-transcriptome analysis with the expression of surface-expressed immune proteins was used to assess the effects of treatments on tumoural leukocytes. Results: Acoustic cavitation was detected in all subjects exposed to BH, causing cellular disruption in tumours 6 h after treatment. Distinct cell clusters were identified in the pancreatic tumours 24 h post-treatment. These included neutrophils and cytotoxic T cells overexpressing genes associated with an N2-like and an exhaustion phenotype, respectively. Reovirus decreased macrophages, and BH decreased regulatory T cells compared to controls. The combined treatments increased neutrophils and the ratio of various immune cells to Treg. All treatments overexpressed genes associated with an innate immune response, while ultrasound treatments downregulated genes associated with the transporter associated with antigen processing (TAP) complex. Conclusions: Our results show that the combined BH and reovirus treatments maximise the overexpression of genes associated with the innate immune response compared to that seen with each individual treatment, and illustrate the anti-immune phenotype of key immune cells in the pancreatic tumour microenvironment. 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

In recent years, passive acoustic monitoring (PAM) technology has significantly contributed to advancements in aquaculture techniques, system iterations, and increased production yields within intelligent feeding systems for Penaeus vannamei. However, current PAM-based intelligent feeding systems do not incorporate environmental factors into the decision process, limiting the improvement of monitoring accuracy in complex environments such as ponds. To establish a connection between environmental factors and the feeding acoustics of P. vannamei, this study utilized PAM technology combined with video analysis to investigate the effects of three key environmental factors—temperature, ammonia nitrogen, and nitrite nitrogen—on the feeding behavioral characteristics of shrimp, with a specific focus on acoustic signals “clicks”. The results demonstrated a significant correlation between the number of clicks and feed consumption in shrimp across different treatments, establishing this stable relationship as a reliable indicator for assessing shrimp feeding status. When water temperature increased from 20 °C to 32 °C, shrimp feed consumption showed an elevation from 0.46 g to 0.95 g per 30 min, with the average number of clicks increasing from 388 to 2947.58 and sound pressure levels rising accordingly. Conversely, ammonia nitrogen at 12 mg/L reduced feed consumption by 0.15 g and decreased click counts by 911.75 pulses compared to controls, while nitrite nitrogen at 40 mg/L similarly suppressed feed consumption by 0.15 g and the average number of clicks by 304.75. A rise in water temperature stimulated shrimp behaviors such as feeding, swimming, and foraging, while elevated concentrations of ammonia nitrogen and nitrite nitrogen significantly inhibited shrimp activity. Redundancy analysis revealed that temperature was the most prominent factor among the three environmental factors influencing shrimp feeding. This study is the first to quantify the specific effects of common environmental factors on the acoustic feeding signals and feeding behavior of P. vannamei using PAM technology. It confirms the feasibility of using PAM technology to assess shrimp feeding conditions under diverse environmental conditions and the necessity of integrating environmental monitoring modules into future feeding systems. This study provides behavioral evidence for the development of precise feeding technologies and the upgrade of intelligent feeding systems for P. vannamei. 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

Acoustic metamaterials (AMs) are artificially structured materials composed of subwavelength units that enable acoustic phenomena not achievable with conventional materials and structures. This review defines and presents a distinct category referred to as soft acoustic metamaterials (SAMs), which use soft materials or reconfigurable structures to achieve enhanced acoustic functionality. These systems make use of the inherent flexibility of their materials or the deformability of their geometry to support passive, active, and adaptive functions. To capture this structural and functional diversity, we introduce a three-dimensional classification that considers geometry, acoustic control mechanisms, and functional responsiveness as interrelated aspects. The geometry is classified into two-dimensional metasurfaces and three-dimensional bulk structures. The control mechanisms include local resonance, phase modulation, attenuation, and structural reconfiguration. The response type refers to whether the system behaves passively, actively, or adaptively. Using this approach, we provide an overview of representative implementations and compare different design approaches to highlight their working principles and application areas. This review presents a structured classification for soft acoustic metamaterials and offers a foundation for future research, with broad potential in intelligent sound systems, wearable acoustics, and architectural applications. Full article

The population of southern fin whales (Balaenoptera physalus quoyi) was severely depleted by 19th and 20th century whaling. Its conservation status remains ‘vulnerable’, as recovery has been slow. Over 19 years of underwater acoustic recordings from the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO)’s hydrophones off Cape Leeuwin, Western Australia, were analyzed to monitor fin whales’ annual migration from their Southern Ocean feeding grounds (where they spend the austral summer) to their tropical breeding grounds (where they spend the austral winter) and back. Northward migrants arrived ~2 d/y earlier (2002–2020). The number of hours with fin whale acoustic presence increased by ~49 h/y and the number of days with fin whale acoustic presence by ~3 d/y. Thus, by the end of the 19-year recording period, fin whales were acoustically present on 74 more days than at the beginning of recording. While changes in habitat function, climate, and ambient noise may affect migratory behavior, the most likely explanation is a post-whaling increase in the number of animals of this Southern Hemisphere subspecies. Full article

The signal-processing architecture of passive surface acoustic wave (SAW) sensors presents significant implementation challenges due to its radar-like operational principle and the inherent complexity of discrete component-based hardware design. While System-in-Package (SiP) has demonstrated remarkable success in miniaturizing electronic systems for smartphones, automotive electronics, and IoT applications, its potential for revolutionizing SAW sensor interrogator design remains underexplored. This paper presents a novel architecture that synergistically combines time-domain ADC design with SiP-based miniaturization to achieve unprecedented simplification of SAW sensor readout systems. The proposed time-domain ADC incorporates an innovative delay chain calibration methodology that integrates physical unclonable function (PUF) principles during time-to-digital converter (TDC) characterization, enabling the simultaneous generation of unique system IDs. The experimental results demonstrate that the integrated security mechanism provides variable-length bit entropy for device authentication, and has a reliability of 97.56 and uniqueness of 49.43, with 53.28 uniformity, effectively addressing vulnerability concerns in distributed sensor networks. The proposed SiP is especially suitable for space-constrained IoT applications requiring robust physical-layer security. This work advances the state-of-the-art wireless sensor interfaces by demonstrating how time-domain signal processing and advanced packaging technologies can be co-optimized to address performance and security challenges in next-generation sensor systems. Full article

This study investigates the complementary noise control capabilities of two passive jet noise mitigation strategies: a traditional ejector nozzle and a novel application of 3M™ Nextel™ 312 ceramic fabric as a thermal–acoustic liner on the central cone of a micro turbojet nozzle. Three nozzle configurations, baseline, ejector, and Nextel-treated, were evaluated under realistic operating conditions using traditional and advanced acoustic diagnostics applied to data from a five-microphone circular array. The results show that while the ejector provides superior directional suppression and low-frequency redistribution, making it ideal for far-field noise control, it maintains high total energy levels and requires structural modifications. In contrast, the Nextel lining achieves comparable reductions in overall noise, especially in high-frequency ranges, while minimizing structural impact and promoting spatial energy dissipation. Analyses in both the time-frequency and spatial–spectral domains demonstrate that the Nextel configuration not only lowers acoustic energy but also disrupts coherent noise patterns, making it particularly effective for near-field protection in compact propulsion systems. A POD analysis further shows that NEXTEL more evenly distributes energy across mid-order modes, indicating its role in smoothing spatial variations and dampening localized acoustic concentrations. According to these results, ceramic fabric linings offer a lightweight, cost-effective solution for reducing the high noise levels typically associated with drones and UAVs powered by small turbojets. When combined with ejectors, they could enhance acoustic suppression in compact propulsion systems where space and weight are critical. Full article

In recent years, with the wide application of underwater vehicles, the vibration and noise problems generated during their operation have attracted great attention from the academic community. Compared with the field of traditional mechanical noise, research on vibration control of the noise that is transiently excited underwater still has significant deficiencies in terms of its theoretical depth and systematicity. In this paper, we take transient noise control for underwater vehicles as the engineering entry point; systematically explain the vibration mechanisms and dynamic characteristics of underwater double-layered cylindrical shell structures; and discuss the vibration transmission paths and the development trends in the control technology in depth. This study mainly includes the following contents. Firstly, the vibration response mechanisms of underwater double-layered cylindrical shells are sorted through a bibliometric analysis, and the evolution laws for plate–shell structures and the vibration transmission paths for single–double-shell structures are summarized systematically; secondly, the multi-path vibration transmission characteristics of double-layered cylindrical shells are analyzed based on energy transfer theory, and the contribution to transient noise through different transmission paths is quantitatively evaluated; and thirdly, the vibration transmission characteristics of active control, passive control, and hybrid control are evaluated systematically in terms of the dimensions of the control mechanism. Then, the engineering applicability of active, passive, and hybrid control technologies is systematically reviewed. Finally, combined with the development of new intelligent materials and adaptive algorithms, the prospective outlook for vibration control technology for shell structures under transient excitation conditions is presented. Full article

Unlike traditional vision-based camera tracking, human indoor localization and activity trajectory recognition also employ other methods such as infrared tracking, acoustic localization, and locators. These methods have significant environmental limitations or dependency on specialized equipment. Currently, WiFi-based human sensing is a novel and important method for human activity recognition. However, most WiFi-based activity recognition methods have limitations, such as using WiFi fingerprints to identify human activities. They either require extensive sample collection and training, are constrained by a fixed environmental layout, or rely on the precise positioning of transmitters (TXs) and receivers (RXs) within the space. If the positions are uncertain, or change, the sensing performance becomes unstable. To address the dependency of current WiFi indoor human activity trajectory reconstruction on the TX-RX position, we propose WiPIHT, a stable system for tracking indoor human activity trajectories using a small number of commercial WiFi devices. This system does not require additional hardware to be carried or locators to be attached, enabling passive, real-time, and accurate tracking and trajectory reconstruction of indoor human activities. WiPIHT is based on an innovative CSI channel analysis method, analyzing its autocorrelation function to extract location-independent real-time movement speed features of the human body. It also incorporates Fresnel zone and motion velocity direction decomposition to extract movement direction change patterns independent of the relative position between the TX-RX and the human body. By combining real-time speed and direction curve features, the system derives the shape of the human movement trajectory. Experiments demonstrate that, compared to existing methods, our system can accurately reconstruct activity trajectory shapes even without knowing the initial positions of the TX or the human body. Additionally, our system shows significant advantages in tracking accuracy, real-time performance, equipment, and cost. Full article

The accurate automatic classification of underwater acoustic signals from passive SoNaR is vital for naval operational readiness, enabling timely vessel identification and real-time maritime surveillance. This study evaluated seven supervised machine learning algorithms for ship identification using passive SoNaR recordings collected by the Brazilian Navy. The dataset encompassed 12 distinct ship classes and was processed in two ways—full-resolution and downsampled inputs—to assess the impacts of preprocessing on the model accuracy and computational efficiency. The classifiers included standard Support Vector Machines, K-Nearest Neighbors, Random Forests, Neural Networks and two less conventional approaches in this context: Linear Discriminant Analysis (LDA) and the XGBoost ensemble method. Experimental results indicate that data decimation significantly affects classification accuracy. LDA and XGBoost delivered the strongest performance overall, with XGBoost offering particularly robust accuracy and computational efficiency suitable for real-time naval applications. These findings highlight the promise of advanced machine learning techniques for complex multiclass ship classification tasks, enhancing acoustic signal intelligence for military maritime surveillance and contributing to improved naval situational awareness. Full article

Soundscape is the sonic environment that a living being, like a human or animal, experiences in a certain setting. It affects how a space functions and how the being perceives its quality. Consequently, the soundscape is crucial in ecosystems globally. In recent decades, researchers have explored soundscapes using various methodologies across different disciplines. This study aims to provide a brief overview of the soundscape research history, pinpoint key authors, institutions, and collaboration networks, and identify trends and main themes through a bibliometric analysis. A search in the Scopus database on 26 February 2025 found 5825 articles, reviews, and conference papers on soundscape published from 1985 to 2024. The analysis indicated a significant increase in soundscape publications, rising from 1 in 1985 to 19 in 2002, and reaching 586 in 2024. J. Kang was the most prolific author with 265 publications, while University College London emerged as the most productive institution. Co-citation analysis revealed three research groups: one focused on urban soundscapes, another on aquatic soundscapes, and a third on soundscapes in landscape ecology. The keyword co-occurrence analysis identified three themes: “soundscape(s), acoustic environment, and urban planning”, “noise, animal(s), bioacoustics, biodiversity, passive acoustic monitoring, fish, and bird(s)”, and “human(s), sound, perception, and physiology”. Full article

The environmental drivers of fin whale (Balaenoptera physalus) acoustic presence in Eastern Antarctic waters were investigated based on passive acoustic recordings from four sites, 2013–2019. Fin whale 20 Hz pulses were detected from late austral summer to early winter. Daily values of sea-ice concentration (SIC) were compared with the number of days with fin whale 20 Hz acoustic presence using a generalized additive model approach. At the Southern Kerguelen Plateau, Casey, and Dumont d’Urville sites, SIC correlated with fin whale calling activity, but less so at the Prydz site. Changes in SIC between sites resulted in variation in acoustic presence: Earlier sea-ice formation at Dumont d’Urville resulted in less acoustic presence in comparison to the Southern Kerguelen Plateau, where sea ice formed later in the season. Interannual variability in SIC impacted yearly acoustic presence, with a later onset of high SIC resulting in greater acoustic presence and later departure (migration timing) of the animals. Identifying the environmental drivers of fin whale presence is key to informing how this migratory species may be affected by environmental variability resulting from climate change. Full article