Acoustic Detection Research Articles

DRR-based acoustic detection model for estimating room shape

An acoustic detection model based on the direct-to-reverberant energy ratio (DRR) is proposed to estimate room shape with audible sound. Besides the direct propagation, the first-order reflection is considered in the calculation of the DRR in this work. Thus, the direct-to-reflected acoustic path ratio is estimated in the proposed model to compute the reflection point, which is used to calculate the pose estimate of the reflector. The mapping of the proposed model is completed by fusing the pose estimates of walls with the spatio-temporal diversity. An initialization process is developed to obtain the initial value of the detection model. An acquisition system working under audible frequencies is built. The experimental results show that even with a low signal-to-diffuse noise ratio (−10 dB), the error of the proposed method is still less than 0.5 degrees in angle and 0.03 m in distance, demonstrating the robustness of the proposed model under the environment with multipath reflections.

Capacitive Low-Frequency Hydrophone Based on Micronanostructured Iontronic Hydrogel for Underwater Monitoring.

Hydrophones play a crucial role in underwater target detection within sonar systems. However, existing hydrophones often encounter challenges such as low sensitivity and poor signal-to-noise ratio (SNR) in the detection of low-frequency acoustic signals. This work introduces a capacitive hydrophone (CH) designed for highly sensitive detection of low-frequency underwater sound signals. Comprising a latex film/silver electrode and a structured hydrogel as the electrolyte layer, the CH is enclosed in a cylindrical casing. By strategically integrating a carbon nanotube (CNT) topology network within a pyramid microarray in the hydrogel, the sensor efficiently forms the electric double layer (EDL), enhancing sensitivity and precision. The CH showcases exceptional low-pressure sensitivity across a wide frequency spectrum (20 to 800 Hz), achieving a receiving sensitivity of up to -159.7 dB in the critical low-frequency band (20 to 125 Hz), surpassing the performance of the commercial hydrophone (RHC-14) by a substantial margin of 33.29 dB. Furthermore, the CH maintains a superior SNR, enabling the detection of sound waves as faint as 0.3 Pa. This study demonstrates the capabilities of the CH in detecting maritime vessels and underwater sounds, underscoring the potential of the CNT-enhanced EDL sensing mechanism for future low-frequency hydrophone design.

Dominant duct azimuthal acoustic mode detection considering failed wall-installed microphones with amplitude or phase measurement biases

Reliable duct mode detection results are essential for aero-engine health condition monitoring and low-noise design. Harsh aero-engine measurement environments may cause biases in amplitude and phase measurements due to the failure of wall-mounted microphones, which will decrease the performance of advanced sparse representation algorithms for duct mode detection. In this paper, two strategies to detect the duct mode with the failed microphones are proposed. An optimization problem is constructed considering the low-rankness of theoretical array measurement and row-sparsity of the failed microphone measurements. The Strategy 1 is based on the recovery of the theoretical array measurement. The recovered array measurement can be further used to detect the duct mode via the sparsity-induced duct mode detection algorithm (generalized minimax-concave penalty in this paper). With the simulation, the Strategy 1 can not recover the theoretical array measurement completely. The Strategy 2 is based on the removal of the failed microphone measurements. With the simulation and experiment, it turns out that the Strategy 2 can detect the interested mode accurately.

Localization and detection of underwater acoustic communication signals using convolutional recursive neural networks

Abstract This paper employs a multi-task neural network (UWAELD) to simultaneously perform the Underwater Acoustic Communication Event Detection (UACED) and direction-of-arrival (DOA) mission. Typically, the UACED and DOA tasks are executed separately, as the UACED task is identified through time-frequency analysis, while the DOA task is identified through the amplitude and phase differences of the signal, making it very difficult to jointly perform both tasks. To overcome this, we utilize a logarithmic spectrogram for time-frequency analysis and stack the normalized eigenvectors obtained by processing the signal covariance matrix. We ultimately evaluate the network’s feasibility using simulation data and lake test data, utilizing acoustical signals measured in actual experiments. Finally, for the UADCED task, the error rate is only 6%, with an F1 score of 0.83; for the DOA task, the localization error is only 1.72°, with a localization recall score of 0.87. Additionally, for the DOA task, we employ the classical MUSIC algorithm to demonstrate the network’s superiority.

First evidence of white sharks, Carcharodon carcharias, in the tongue of the ocean, central Bahamas

The white shark, Carcharodon carcharias, is an iconic apex predator, playing an important ecological role across its range. Persistent bycatch and overfishing led to white shark declines, but recent studies in the North Western Atlantic (NWA) revealed evidence for regional recovery, and highlighted the importance of Southeastern Florida and the Gulf of Mexico as overwintering grounds for maturing white sharks. However, despite its proximity to Florida and comparably productive habitats, records of white sharks in The Bahamas are extremely rare, with a comprehensive survey of sightings and captures describing only one white shark between 1800 - 2010. Here, we reveal acoustic tracking detections of ten white sharks from 2020 - 2024 along the western edge of the Tongue of the Ocean off Central Andros Island, The Bahamas. White sharks were originally tagged off the coast of the United States and Canada, and detected off Andros Island, The Bahamas from November-May. White sharks were detected along the drop-off zone of the reef at ca. 25 m, exclusively between dusk and dawn, with the number of detections suggesting transient behavior. These findings expand our knowledge of white shark distribution in the NWA, highlighting data gaps in The Bahamas and underlining the importance of collaborative protective measures for species recovery.

Acoustic Rapid Detection Technology and Its Application for Rare Earth Element (REE)-Rich Sediments in the Pigafetta Basin of the Western Pacific

This study aims to investigate the stratigraphic features and rare earth element (REE) mechanisms of deep-sea REE-rich sediments in the West Pacific Pigafetta Basin using acoustic rapid detection technology. Through an analysis of sub-bottom profile data and synthesis of existing studies, this study reveals the acoustic properties and thickness distribution of the REE-rich sediments. Acoustic spectral records identify three distinct acoustic facies: opaque (O), transparent (T), and laminated (L). This study maps the thickness and spatial distribution of the REE-rich sediment layer in the research area, ranging from approximately 6 to 36 m in thickness. Regions with REE-rich sediments exceeding 30 m in depth are identified, showing concentrated distribution along the northwest–southeast axis and a contiguous zone in the southwest corner of the study area. The method employed in this study can determine the potential bottom boundary of the REE-rich layer by assessing the thickness range of the sedimentary layer, overcoming limitations of traditional sampling methods. Furthermore, the thickness distribution characteristics of the REE-rich sedimentary layer in the study area provide valuable insights for future research on resource evaluation and estimation.

Elliptical micropillars for efficient generation and detection of coherent acoustic phonons

Coherent acoustic phonon generation and detection assisted by optical resonances are at the core of efficient optophononic transduction processes. However, when one is dealing with a single optical resonance, the optimum generation and detection conditions occur at different laser wavelengths, i.e., different detunings from the cavity mode. In this work, we theoretically propose and experimentally demonstrate the use of elliptical micropillars to reach these conditions simultaneously at a single wavelength. Elliptical micropillar optophononic resonators present two optical modes with orthogonal polarizations at different wavelengths. By using a cross-polarization-scheme pump-probe experiment, we exploit the mode splitting and couple the pump beam to one mode while the probe is detuned from the other mode. In this way, at a particular micropillar ellipticity, the phonon-generation and phonon-detection processes are both enhanced. We report an enhancement of the coherent-phonon-generation-detection process by a factor of approximately 3.1 when comparing the highest achievable signals from elliptical and circular micropillars. Our findings constitute a step forward in tailoring light-matter interaction for more-efficient ultrahigh-frequency optophononic devices. Published by the American Physical Society 2024

Fish Acoustic Detection Algorithm Research: a deep learning app for Caribbean grouper calls detection and call types classification

In this paper, we present the first machine learning package developed specifically for fish calls identification within a specific range (0–500Hz) that encompasses four Caribbean grouper species: red hind (E. guttatus), Nassau (E. striatus), yellowfin (M. venenosa), and black (M. bonaci). Because of their ubiquity in the soundscape of the grouper’s habitat, squirrelfish (Holocentrus spp.) sounds along with vessel noise are also detected. In addition the model is also able to separate grouper species call types. This package called FADAR, the Fish Acoustic Detection Algorithm Research is a standalone user-friendly application developed in Matlab™. The concept of FADAR is the product of the evaluation of various deep learning architectures that have been presented in a series of published articles. FADAR is composed of a main algorithm that can detect all species calls including their call types. The architecture of this model is based on an ensemble approach where a bank of five CNNs with randomly assigned hyperparameters are used to form an ensemble of classifiers. The outputs of all five CNNs are combined by a fusion process for decision making. At the species level, the output of the multimodel is thus used to classify the calls in terms of their types. This is done by species specific deep learning models that have been thoroughly evaluated in the literature on the species concerned here, including transfer learning for red hind and yellowfin groupers and custom designed CNN for Nassau grouper, which has a greater number of known call types than the other species. FADAR was manually trained on a diversity of data that span various regions of the Caribbean Sea and also two recorder brands, hydrophone sensitivities, calibrations and sampling rates, including a mobile platform. This strategy has conferred FADAR substantive robustness to a diversity of noise level and sources that can be found in the grouper calls frequency band such as vessels and marine mammals. Performance metrics based on sensitivity (recall) and specificity showed the same performance level for both balanced and unbalanced datasets and at locations not used in the training set.

Six Square Miles of Urban America: Association Between Firearm Discharge, Injury, and Fatality.

Despite the increase in firearm injury observed across the country, significant gaps remain relevant to our understanding of how firearm exposure translates to injury. Using acoustic gunshot detection and a collaborative hospital and law enforcement firearm injury database, we sought to identify the relationship between firearm discharge and injury over time. From 2018 to 2021, instances of firearm discharge captured via acoustic detection in 6 square miles of Louisville, KY, were merged with data from the collaborative firearm injury database. Key outcomes included the total number of rounds fired, injury and fatality rates per round, and the percentage of rounds discharged from automatic weapons and high-capacity magazines. During the study period, 54,397 rounds of ammunition were discharged resulting in 914 injuries, 435 hospital admissions, 2,442 hospital days, 155 emergent operations, and 180 fatalities. For each round of ammunition fired, the risk of injury and fatality was 1.7% and 0.3%, respectively. The total number of rounds fired per month nearly tripled (614 vs 1,623, p < 0.001) leading to increased injury (15 vs 37, p < 0.001) and fatality (3 vs 7, p < 0.001). The percentage of rounds fired from automatic weapons (0 vs 6.8%, p < 0.001) and high-capacity magazines (7.6 vs 28.9%, p < 0.001) increased over time. The increased burden of firearm injury is related to an overall increase in firearm exposure as measured by the total number of rounds discharged. High-capacity magazines and automatic weaponry are being used with increasing frequency in urban America.

Partial discharge monitoring by improved PGC-arctan algorithm

This paper proposes an improved PGC-Arctan demodulation algorithm for detecting partial discharge signals in transformers. Compared with the traditional PGC-Arctan algorithm, the improved algorithm extracts carrier frequency difference and carrier phase delay by fundamental mixing and using low-pass filtering on the interference signal, and compensates the phase modulation depth by high order frequency, reducing THD and enhancing SNR. The algorithm is theoretically analyzed, simulated and verified through experiments. In the 200 kHz acoustic emission signal detection experiment, the SNR and THD of the improved algorithm are 49.63 dB and 1.34%, respectively, which is 13.59 dB higher than the SNR of the traditional algorithm, and the THD is reduced by 76.61%, and the harmonic distortion is well suppressed. The system is used to detect weak ultrasonic signals generated during partial discharge of transformers and compare them with PZT. The results show that the system has the potential to be used to monitor the partial discharge signal in transformers.

Two listeners detect slightly more birds than a single listener when interpreting acoustic recordings

ABSTRACT Acoustic recorders are increasingly important for monitoring bird populations and have potential to augment existing monitoring programs such as the North American Breeding Bird Survey (BBS). An advantage of acoustic recordings is that they can be reviewed multiple times by multiple experts, potentially yielding improved estimates of species abundance and community richness. Yet, few studies have examined how frequently successive listeners disagree on acoustic interpretations and how strongly estimates of species richness and abundance are altered when multiple experts review each recording. We assigned multiple expert listeners to interpret recordings at 690 BBS stops, and subsequently assigned second listeners to conduct a review of first listeners’ interpretations. We examined the extent to which listeners agreed with each other and quantified the effect of disagreements on resultant estimates of species occurrence, abundance, and stop-level richness. We also compared estimates from acoustic recordings to those obtained during simultaneous field surveys. Estimates were highly correlated for number of species per stop (r = 0.92) and detection probabilities of species (r = 0.97) based on first and second-listener data. Second listeners disagreed with ~9% of first listeners’ interpretations and added an average of ~15% additional species and 16% additional birds not reported by first listeners. Estimates based on acoustic recordings were also highly correlated with those obtained from field surveys, though listeners were unable to count flocks. A single expert reviewer can provide a reasonable approximation of the relative abundance and species composition of birds available for acoustic detection during BBSs. However, acoustic review by multiple listeners may still be important for species that are rare, difficult to identify, or of high conservation concern.

Distributed Partial Discharge Acoustic Signal Detection and Localization Technology for GIL With Built-in Fiber Optics

Distributed Partial Discharge Acoustic Signal Detection and Localization Technology for GIL With Built-in Fiber Optics

Improved method for drone sound event detection system aiming at the impact of background noise and angle deviation

Drones pose a hidden threat to public safety, and effective and accurate detection technology for the drone that exist in the environment is imminent, in which how to weaken the influence of target sound source localization deviation and strong background interference noise on the detection task is the key to improve the accuracy of drone sound event detection. In this paper, a method has been proposed to improve the accuracy of drone acoustic event detection, named as the linear shrinkage-subspace projection-power spectral density filter method (LSP). This method mainly including covariance matrix reconstruction, steering vector recalibration, and filter coefficient redesign method. Firstly, based on Minimum Variance Distortionless Response, the linear shrinkage method is used to suppress the interference and noise components in the signal plus interference covariance matrix, and the sample covariance matrix is reconstructed to eliminate background interference noise. Then, the correlation between the steering vector and the eigenvector is used to eliminate the angle correlation term, and the subspace projection method is combined to recalibrate the steering vector, so as to improve the ability of the beamforming method to resist the angle deviation and realize the correction of the target source positioning deviation. Next, a redesign method for wiener filter coefficients based on the estimated power spectral density is used to further weaken background interference noise. In order to verify the accuracy of the proposed method, a complete drone sound event detection system is constructed by combining the deep learning drone sound event detection classifier, and the evaluation is carried out according to different angular deviations and interference sound distances. In addition, a new evaluation criterion is proposed, named as the Machine-Human Extreme Hearing Distance Rate (MHDR), which analogizes the system's detection ability with the ear's auditory detection ability. The research results of this article indicate that the detection accuracy of the detection system shows satisfactory accuracy when the proposed method is applied to circular microphone array, that improved by 15.12 % compared to existing methods. The proposed method improves the detection accuracy of the drone acoustic event detection task under the influence of sound source position deviation and strong background speech interference, and provides a reference for the development of anti-drone technology.

Microclimatic drivers of winter bat activity in coast redwood forests.

Bats are among the least well-known mammals, particularly in terms of their behavior and activity patterns during the winter. Here, we use passive acoustic monitoring to overcome some of the challenges inherent in surveying cryptic forest bats during the wet season to quantify overwintering behavior for 11 species in California coast redwood forests under varying microclimates. Because different species are active at different forest heights, we also examined the effect of acoustic detector placement (treetop or ground level). Generalized linear mixed models were used to relate acoustic detection probability for 8 species to daytime and nighttime temperature, relative humidity, water vapor pressure, and detector placement. The results indicate that daytime maximum temperature best explained variation in nightly probability of detection, and temperature threshold at which bats were predicted to be detected varied considerably across species. By using more precise species detection methods, we were able to resolve significant differences in activity patterns between Myotis yumanensis and M. californicus, 2 species with similar acoustic signatures that are often lumped together. Myotis californicus was predicted to have a 50% probability of detection at maximum daytime temperature as low as 12.5 °C, whereas M. yumanensis was not predicted to have 50% detection probability until maximum daytime temperature was at least 22 °C, suggesting that M. californicus spends less time in torpor. Also, monitoring at the top of the canopy revealed 4 migratory species to be present in the ecosystem on significantly more monitoring nights than could be observed using conventional ground-based monitoring methods. Improving winter bat survey methods provides evidence that diverse bat species are more active in redwood forests during the winter than previously documented. This finding suggests that coastal forests could provide important winter bat habitat for both resident and migratory species.

Challenges and advances in two-dimensional photoacoustic computed tomography: a review.

Photoacoustic computed tomography (PACT), a hybrid imaging modality combining optical excitation with acoustic detection, has rapidly emerged as a prominent biomedical imaging technique. We review the challenges and advances of PACT, including (1)limited view, (2)anisotropy resolution, (3)spatial aliasing, (4)acoustic heterogeneity (speed of sound mismatch), and (5)fluence correction of spectral unmixing. We performed a comprehensive literature review to summarize the key challenges in PACT toward practical applications and discuss various solutions. There is a wide range of contributions from both industry and academic spaces. Various approaches, including emerging deep learning methods, are proposed to improve the performance of PACT further. We outline contemporary technologies aimed at tackling the challenges in PACT applications.

Detection of Large Dynamic Range Acoustic Signal Using OPD-Based Fiber-Optic Interferometric Demodulation With SNR Enhancement

Detection of Large Dynamic Range Acoustic Signal Using OPD-Based Fiber-Optic Interferometric Demodulation With SNR Enhancement

Acoustic Emission Detection in Noisy Environments using Linear Prediction

In non-destructive testing, acoustic emission testing is an established technique with numerous applications in academia and the industry. In nearly all applications, the detection of relevant acoustic emission signals in continuous structure-borne sound recordings is a crucial step that forms the basis for subsequent analyses and should therefore neither miss any relevant acoustic emissions nor detect too many irrelevant events. That is because, in its most prevalent form, acoustic emission testing utilizes ultrasound signals that require large amount of storage, which might be an economically limiting factor especially for structural health monitoring of large civil infrastructures. In these monitoring applications, the signal-to-noise ratio is also expected to be worse compared to more controlled laboratory conditions, as they can be often found, for example, in materials research. We therefore propose the use of linear prediction, a time series forecasting technique, to extract relevant acoustic emissions from continuous recordings. The proposed methodology utilizes the residual between the signal predicted from a linear combination of previous time steps and the actual measurement to detect any anomalous events. Since the linear predictor can be initialized using the environmental noise only, no specific knowledge of the acoustic emission signals of interest is required beforehand and hence can automatically adapt to each measurement environment. We compare our approach with a simple amplitude-based detection as it is commonly implemented in commercially available acoustic emission systems. Especially for low signal-to-noise ratios, an increase in the area under the receiver operating characteristic curve of up to 0.7 compared to the amplitude-based detection is found.

Microwave irradiation-induced deterioration of rock mechanical properties and implications for mechanized hard rock excavation

In this study, a novel microwave-water cooling-assisted mechanical rock breakage method was proposed to address the issues of severe tool wear at elevated temperatures, poor rock microwave absorption, and excessive microwave energy consumption. The investigation object was sandstone, which was irradiated at 4 kW microwave power for 60 s, 180 s, 300 s, and 420 s, followed by air and water cooling. Subsequently, uniaxial compression, Brazilian tension, and fracture tests were conducted. The evolution of damage in sandstone was measured using active and passive nondestructive acoustic detection methods. The roughness of the fracture surfaces of the specimens was quantified using the box-counting method. The damage mechanisms of microwave heating and water cooling on sandstone were discussed from both macroscopic and microscopic perspectives. The experimental results demonstrated that as the duration of the microwave irradiation increased, the P-wave velocity, uniaxial compressive strength (UCS), elastic modulus (E), tensile strength, and fracture toughness of sandstone exhibited various degrees of weakness and were further weakened by water cooling. Furthermore, an increase in the microwave irradiation duration enhanced the damaging effect of water cooling. The P-wave velocity of the sandstone was proportional to the mechanical parameters. Microwave heating and water cooling weakened the brittleness of the sandstone to a certain extent. The fractal dimension of the fracture surface was correlated with the duration of microwave heating, and the water-cooling treatment resulted in a rougher fracture surface. An analysis of the instantaneous cutting rate revealed that water cooling can substantially enhance the efficiency of microwave-assisted rock breakage.

A deep learning method for reflective boundary estimation

Environment estimation is a challenging task in reverberant settings such as the underwater and indoor acoustic domains. The locations of reflective boundaries, for example, can be estimated using acoustic echoes and leveraged for subsequent, more accurate localization and mapping. Current boundary estimation methods are constrained to high signal-to-noise ratios or are customized to specific environments. Existing methods also often require a correct assignment of echoes to boundaries, which is difficult if spurious echoes are detected. To evade these limitations, a convolutional neural network (NN) method is developed for robust two-dimensional boundary estimation, given known emitter and receiver locations. A Hough transform-inspired algorithm is leveraged to transform echo times of arrival into images, which are amenable to multi-resolution regression by NNs. The same architecture is trained on transform images of different resolutions to obtain diverse NNs, deployed sequentially for increasingly refined boundary estimation. A correct echo labeling solution is not required, and the method is robust to reverberation. The proposed method is tested in simulation and for real data from a water tank, where it outperforms state-of-the-art alternatives. These results are encouraging for the future development of data-driven three-dimensional environment estimation with high practical value in underwater acoustic detection and tracking.

Habitat selection and spatial behaviour of vulnerable juvenile lemon sharks: Implications for conservation

Nearshore environments represent important habitat for many marine vertebrates during their early-life stages. Globally, these coastal sites are impacted by human activities that have the potential to negatively impact biodiversity in ways we do not yet fully appreciate. To improve our understanding of the relevance of mangrove removal in tropical elasmobranch nursery grounds, we studied the globally Vulnerable lemon shark (Negaprion brevirostris) in a mangrove-fringed lagoon in Bimini, The Bahamas, following a decade of coastal development and habitat disruption. We used two years of acoustic telemetry detections and generalised linear mixed models (GLMMs) to evaluate the link between juvenile shark spatial behaviour and six features of their physical environment. AIC-adjusted model-averaged predictions of habitat selection demonstrated that distance from the central mangrove forest was the most important feature for sharks. After updating model averaging to account for overall preference for proximity to the central forest, we found that medium density seagrass was secondarily preferred over all other habitat types (bare sand, sargassum, urban and rocky outcrops, and deep water) within the core use area (probability of use ≥ 50 %). Locally, our results support including this core area in future marine protected area considerations. More broadly, in the face of rapid global population declines of many elasmobranchs and wide-spread habitat fragmentation in coastal marine nurseries, we identified widely applicable habitat features underpinning an area of high ecological significance for a threatened shark during a vulnerable life stage and outlined a habitat selection framework suitable for using marine vertebrate movement data as ecological indicators for future applied conservation.