Click Detection Research Articles

Experimental retrieval of photon statistics from click detection

Experimental retrieval of photon statistics from click detection

Heard off Heard: Acoustic detections of sperm whales (Physeter macrocephalus) and other cetaceans off Heard Island.

An underwater acoustic recorder was moored off Heard Island from September 2017 through March 2018 to listen for marine mammals. Analysis of data was initially conducted by visual inspection of long-term spectral averages to reveal sounds from sperm whales, Antarctic and pygmy blue whales, fin whales, minke whales, odontocete whistles, and noise from nearby ships. Automated detection of sperm whale clicks revealed they were seldom detected from September through January (n = 35 h) but were detected nearly every day of February and March (n = 684 h). Additional analysis of these detections revealed further diel and demographic patterns.

Click detection rate variability of central North Pacific sperm whales from passive acoustic towed arrays.

Passive acoustic monitoring (PAM) is an optimal method for detecting and monitoring cetaceans as they frequently produce sound while underwater. Cue counting, counting acoustic cues of deep-diving cetaceans instead of animals, is an alternative method for density estimation, but requires an average cue production rate to convert cue density to animal density. Limited information about click rates exists for sperm whales in the central North Pacific Ocean. In the absence of acoustic tag data, we used towed hydrophone array data to calculate the first sperm whale click rates from this region and examined their variability based on click type, location, distance of whales from the array, and group size estimated by visual observers. Our findings show click type to be the most important variable, with groups that include codas yielding the highest click rates. We also found a positive relationship between group size and click detection rates that may be useful for acoustic predictions of group size in future studies. Echolocation clicks detected using PAM methods are often the only indicator of deep-diving cetacean presence. Understanding the factors affecting their click rates provides important information for acoustic density estimation.

High Arctic “hotspots” for sperm whales (Physeter macrocephalus) off western and northern Svalbard, Norway, revealed by multi-year Passive Acoustic Monitoring (PAM)

Despite the well-documented, broad global distribution of sperm whales (Physeter macrocephalus), their distributional patterns remain poorly known in Arctic regions, where year-round monitoring is challenging. Adult male sperm whales are known to migrate seasonally between nutrient-rich high latitude waters and low latitude breeding grounds. However, knowledge is limited regarding fine-scale distribution and seasonal presence at high latitudes. To investigate the acoustic occurrence of this vocally active species in the High Arctic of the Northeast Atlantic, this study combined automated and manual click detection methods to analyze passive acoustic data collected at eight locations around the Svalbard Archipelago, Norway, between 2012 and 2021. The results revealed the presence of sperm whales at six recording sites and demonstrated sperm whale “hotspots” in ice-free areas in eastern Fram Strait along the shelf break and close to the west coast of Spitsbergen from May–January, with some variation between years and locations. Although acoustic presence decreased with increasing latitude, even the northern-most location (81° N) recorded sperm whale vocal activity between August and January. This study provides a baseline for sperm whale acoustic presence in the High Arctic, which will be essential in the context of detecting future changes and also for predicting future distribution patterns in the rapidly changing Arctic marine environment.

Acoustic twilight: A year‐long seafloor monitoring unveils phenological patterns in the abyssal soundscape

AbstractDespite the perpetual darkness of the deep sea, contrasting the sunlit epipelagic waters, many deep‐sea organisms exhibit rhythmic activities. To discern environmental cues that may serve as entrainment signals for deep‐sea organisms, this study investigated the soundscape of the abyssal plain south of Minamitorishima Island. Our analysis revealed clear diel and seasonal patterns, primarily driven by evening fish choruses and marine mammal vocalizations. These evening choruses, discernible above the background noise, likely serve as a circadian time cue for organisms capable of perceiving them within the aphotic depths. In addition, the frequent detection of whistles and echolocation clicks suggests this region functions as a foraging ground for marine mammals. These acoustic cues might guide organisms with auditory capabilities toward habitats rich in sinking food debris and whale falls. By elucidating the ecological processes shaping abyssal soundscape dynamics, these findings open new directions for further exploration in deep‐sea chronobiology.

Detecting free-ranging river dolphin using deep learning based algorithms

Indus river dolphin is an endangered freshwater river dolphin and is included in IUCN red list of endangered species. Canal-strandings is one of the major issues that results in high mortality of the dolphin because the dolphins that are stranded into canals during flood season cannot return to the river during low-water season. As the dolphin uses high frequency echolocation clicks for navigation in muddy river waters, passive acoustic monitoring can be used for detection and localization of the dolphin and can assist in conservation efforts. In this context, we compare performance of variants of deep learning (LSTM) based event detection algorithms for dolphin click detection with conventional algorithms (such as the Teager Energy based click detection and Envelope Derivative Operator based click detection) and analyze the possible scenarios where machine learning based methods may assist in better performance by automatic detection of reflected clicks.

Automatic click detector for sperm and beaked whale distribution monitoring

Passive acoustic monitoring (PAM) is an effective method for monitoring marine fauna. Automatic detection, identification, and quantification of echolocation clicks by toothed whales is commonly computationally inefficient. We present an autoregression and correlation-based algorithm for detection and classification of odontocete clicks. The detection process consists of three steps: (1) detection and extraction of impulsive signals from PAM recordings, (2) cross-correlation of the extracted sounds with template signals, and (3) binary classification of detected clicks. The extraction of impulsive signals from background noise is based on automatic detection of outliers in an autoregression model of noise. The model settings can be adjusted to detect signals of certain length. The cross-correlation analysis involves the use of template signals of high signal-to-noise ratio, which aremanually identified and extracted from PAM data. Using the cross-correlationcoefficient as a criterion allows distinguishing echolocation clicks by sperm or beaked whales from other impulsive sounds. Finally, binary classification utilizes thresholds determined through the receiver operating characteristic analysis of an independent subset of manually labeled, extracted signals. The approach was tested for detection of sperm and beaked whales in PAM datasets recorded on the Northwest Shelf of Australia and demonstrated low false positive rates of 3%–5%.

Voices from the deep: Odontocete occurrence patterns relative to oceanographic conditions in the northern California Current System

Odontocetes play an important ecological role as apex predators in the northern California Current System (CCS), which is characterized by seasonal rises in primary productivity fueled by wind-driven upwelling of cold nutrient-rich water. This productivity sustains higher trophic-level prey optimal for deep-diving odontocetes including beaked and sperm whales. Due to the cryptic ecology of these whales, there are few systematic studies of their occurrence across seasons in northern CCS waters. The Holistic Assessment of Living marine resources off Oregon (HALO) Project—a collaboration between Oregon State University and Cornell University—addresses this gap via quarterly vessel-based visual surveys and year-round passive acoustic monitoring (PAM). Here, we detect and classify species-specific odontocete acoustic signals using long-term spectral averages, automated click detection, and unsupervised clustering of semi-continuous PAM data from 10/2021 to 12/2022. Site-specific occurrence of beaked and sperm whales is revealed on hourly to seasonal temporal scales at three recording sites along a depth gradient spanning the continental slope (300 and 630 m water depth) and abyssal plain (2860 m). By relating odontocete detections to dynamic oceanographic conditions from nearby profiling moorings, we identify likely drivers of prey availability influencing predator distribution. These results will inform conservation efforts of cryptic odontocetes in the face of rapid environmental change.

Detection of Spatially Localized Sounds Is Robust to Saccades and Concurrent Eye Movement-Related Eardrum Oscillations (EMREOs).

Hearing is an active process, and recent studies show that even the ear is affected by cognitive states or motor actions. One example are movements of the eardrum induced by saccadic eye movements, known as "eye movement-related eardrum oscillations" (EMREOs). While these are systematically shaped by the direction and size of saccades, the consequences of saccadic eye movements and their resulting EMREOs for hearing remain unclear. We here studied their implications for the detection of near-threshold clicks in human participants. Across three experiments, sound detection was not affected by their time of presentation relative to saccade onset, by saccade amplitude or direction. While the EMREOs were shaped by the direction and amplitude of the saccadic movement, inducing covert shifts in spatial attention did not affect the EMREO, suggesting that this signature of active sensing is restricted to overt changes in visual focus. Importantly, in our experiments, fluctuations in the EMREO amplitude were not related to detection performance, at least when monaural cues are sufficient. Hence, while eye movements may shape the transduction of acoustic information, the behavioral implications remain to be understood.SIGNIFICANCE STATEMENT Previous studies suggest that oculomotor behavior may influence how we perceive spatially localized sounds. Recent work has introduced a new perspective on this question by showing that eye movements can directly modulate the eardrum. Yet, it remains unclear whether this signature of active hearing accounts for behavioral effects. We here show that overt but not covert changes in visual attention modulate the eardrum, but these modulations do not interfere with the detection of sounds. Our results provide a starting point to obtain a deeper understanding about the interplay of oculomotor behavior and the active ear.

Application of Wavelet Transform for the Detection of Cetacean Acoustic Signals

Cetaceans are an important part of the ocean ecosystem and are widely distributed in seas across the world. Cetaceans are heavily reliant on acoustic signals for communication. Some Odontoceti can perceive their environments using their sonar system, including the detection, localization, discrimination, and recognition of objects. Acoustic signals are one of the most commonly used types of data for Cetacean research, and it is necessary to develop Cetacean acoustic signal detection methods. This study compared the performance of a manual method, short-time Fourier transform (STFT), and wavelet transform (WT) in Cetacean acoustic signal detection. The results showed that WT performs better in click detection. According to this research, we propose using STFT for whistle and burst-pulse marking and WT for click marking in dataset building. This research will be helpful in facilitating research on the habits and behaviors of groups and individuals, thus providing information to develop methods for protecting species and developing biological resources.

Comparison of visual and passive acoustic estimates of beaked whale density off El Hierro, Canary Islands

Passive acoustic monitoring (PAM) offers considerable potential for density estimation of cryptic cetaceans, such as beaked whales. However, comparative studies on the accuracy of PAM density estimates from these species are lacking. Concurrent, low-cost drifting PAM, with SoundTraps suspended at 200 m depth, and land-based sightings, were conducted off the Canary Islands. Beaked whale density was estimated using a cue-count method, with click production rate and the probability of click detection derived from digital acoustic recording tags (DTags), and distance sampling techniques, adapted to fixed-point visual surveys. Of 32 870 detections obtained throughout 206 h of PAM recordings, 68% were classified as "certain" beaked whale clicks. Acoustic detection probability was 0.15 [coefficient variation (CV) 0.24] and click production rate was 0.46 clicks s - 1 (CV 0.05). PAM density estimates were in the range of 21.5 or 48.6 whales per 1000 km2 [CV 0.50 or 0.44, 95% confidence interval (CI) 20.7-22.4 or 47-50.9), depending on whether "uncertain" clicks were considered. Density estimates from concurrent sightings resulted in 33.7 whales per 1000 km2 (CV 0.77, 95% CI 8.9-50.5). Cue-count PAM methods under application provide reliable estimates of beaked whale density, over relatively long time periods and in realistic scenarios, as these match the concurrent density estimates obtained from visual observations.

Behavioural Responses of Common Dolphins Delphinus delphis to a Bio-Inspired Acoustic Device for Limiting Fishery By-Catch

By-catch is the most direct threat to marine mammals globally. Acoustic repellent devices (pingers) have been developed to reduce dolphin by-catch. However, mixed results regarding their efficiency have been reported. Here, we present a new bio-inspired acoustic beacon, emitting returning echoes from the echolocation clicks of a common dolphin ‘Delphinus delphis’ from a fishing net, to inform dolphins of its presence. Using surface visual observations and the automatic detection of echolocation clicks, buzzes, burst-pulses and whistles, we assessed wild dolphins’ behavioural responses during sequential experiments (i.e., before, during and after the beacon’s emission), with or without setting a net. When the device was activated, the mean number of echolocation clicks and whistling time of dolphins significantly increased by a factor of 2.46 and 3.38, respectively (p < 0.01). Visual surface observations showed attentive behaviours of dolphins, which kept a distance of several metres away from the emission source before calmly leaving. No differences were observed among sequences for buzzes/burst-pulses. Our results highlight that this prototype led common dolphins to echolocate more and communicate differently, and it would favour net detection. Complementary tests of the device during the fishing activities of professional fishermen should further contribute to assessment of its efficiency.

An integrated strategy for monitoring cetaceans in data-poor regions

A fundamental issue in cetacean conservation is determining the occurrence, distribution, and habitat use of target species. However, these baselines are extremely deficient in many poorly investigated regions, making it challenging to inform conservation management initiatives. In this case study, we employed three complementary approaches to generate conservation baselines on a “Vulnerable” near-shore delphinid species, the Indo-Pacific humpback dolphin, Sousa chinensis, in the waters southwest of Hainan (SW Hainan), China. First, in late 2013, we conducted large-scale local ecological knowledge (LEK) surveys with 510 Hainanese fishers. Our LEK findings revealed that SW Hainan was a previously underreported area with numerous fishermen encounters of humpback dolphins dating back to the 1970s. Second, from 2014 to 2019, we conducted monthly boat-based visual surveys in SW Hainan. Our sighting data verified the reliability of previous LEK findings and further confirmed the year-round occurrence and species dominance of humpback dolphins in SW Hainan. Third, we applied a supplementary approach (stationary passive acoustic monitoring, PAM) to monitor dolphin biosonar activities in the region. Our 10-site PAM dataset from February 2018 to June 2019 indicated spatiotemporal variations in dolphin click detections. To conclude, our findings, derived from multiple approaches, provide a scientific basis for cetacean conservation management and marine spatial planning on a regional scale. More generally, this study offers an integrated monitoring strategy consisting of three step-by-step approaches, which can help conservationists, especially those who work in data-deficient waters, obtain critical baseline data on cetaceans in an efficient, timely, and reliable way.

THE USAGE OF GRAPH THEORY CONCEPTS FOR THE COMPLEX NETWORKS ANALYSIS

The article considers and investigates the usage of graph theory concepts for the complex networks analysis. Abstract from their physical nature, the topological properties of these networks are considered, which significantly determine the functioning of networks and are the subject of study of complex networks. Each node of the network can be connected with other nodes by a certain number of connections that may have a direction, or nodes can be connected with each other by symmetrical connections. Also in modern systems of analysis and visualization of networks such concepts as degrees of vertices, ranking, clustering, modularity, algorithms of laying of graphs, etc. are widely used. To calculate the parameters of the network as a whole use the number of nodes, the number of edges, the geodetic distance between nodes, the average distance from one node to another, density – the ratio of the number of edges in the network to the maximum possible number of edges for a given number of nodes; number of triads, diameter of the network (maximum geodetic distance). Structural network analysis includes: click detection (subgroups that are more interconnected than other click nodes); identification of network components; finding bridges (nodes, the removal of which breaks the network into incoherent parts); groups of equivalent nodes (which have the most similar communication profiles). One of the areas of complex networks analysis is their visualization, which allows to obtain important information about the structure and properties of the network without accurate calculations. Software tools for the complex networks analysis support the calculation of all the described parameters of the nodes, the network as a whole, provide its structural analysis and visualization, work with different data formats. For the complex networks analysis, the main visualization tools are described, in the vast majority, freely distributed, free programs.

Performance metrics for marine mammal signal detection and classification.

Automatic algorithms for the detection and classification of sound are essential to the analysis of acoustic datasets with long duration. Metrics are needed to assess the performance characteristics of these algorithms. Four metrics for performance evaluation are discussed here: receiver-operating-characteristic (ROC) curves, detection-error-trade-off (DET) curves, precision-recall (PR) curves, and cost curves. These metrics were applied to the generalized power law detector for blue whale D calls [Helble, Ierley, D'Spain, Roch, and Hildebrand (2012). J. Acoust. Soc. Am. 131(4), 2682-2699] and the click-clustering neural-net algorithm for Cuvier's beaked whale echolocation click detection [Frasier, Roch, Soldevilla, Wiggins, Garrison, and Hildebrand (2017). PLoS Comp. Biol. 13(12), e1005823] using data prepared for the 2015 Detection, Classification, Localization and Density Estimation Workshop. Detection class imbalance, particularly the situation of rare occurrence, is common for long-term passive acoustic monitoring datasets and is a factor in the performance of ROC and DET curves with regard to the impact of false positive detections. PR curves overcome this shortcoming when calculated for individual detections and do not rely on the reporting of true negatives. Cost curves provide additional insight on the effective operating range for the detector based on the a priori probability of occurrence. Use of more than a single metric is helpful in understanding the performance of a detection algorithm.

Cuvier’s beaked whale foraging dives identified via machine learning using depth and triaxial acceleration

Knowledge of Cuvier’s beaked whale Ziphius cavirostris behavior has expanded through the utilization of animal-borne tags. However, many tag types do not record sound—thus preventing echolocation click detections to identify foraging—or have short deployments that sample a limited range of behaviors. As the quantity of such non-acoustic tag data grows, so too does the need for robust methods of detecting foraging from non-acoustic data. We used 692 dives from 5 sound-recording tags on Cuvier’s beaked whales in southern California, USA, to develop extreme gradient boosting tree models to detect foraging based on 1 Hz depth and 16 Hz triaxial acceleration data. We performed repeated 10-fold cross validation using classification accuracy to tune 500 models with randomly partitioned training and testing datasets. An average of 99.9 and 99.2% of training and testing dataset dives, respectively, were correctly classified across the 500 models. Dives without associated sound recordings (n = 2069 from 7 whales including 4 non-acoustic tags) were classified via a model that maximized training information using dive depth and duration, ascent and descent rates, bottom-phase average vertical speed, and roll circular variance during dive descents and bottom phases. Of all long, deep dives (conventionally assumed to include foraging), 2.4% were classified as non-foraging dives, while 0.3% of short, shallow dives were classified as foraging dives. Results confirm that conventional depth and/or duration classifiers provide reasonable estimates of longer-term foraging patterns. However, additional variables previously listed enhance foraging detections for unusual dives (notably non-foraging deep dives) for southern California Cuvier’s beaked whales.

Micro-Behavioral Accidental Click Detection System for Preventing Slip-Based Human Error.

Accidentally clicking on a link is a type of human error known as a slip in which a user unintentionally performs an unintended task. The risk magnitude is the probability of occurrences of such error with a possible substantial effect to which even experienced individuals are susceptible. Phishing attacks take advantage of slip-based human error by attacking psychological aspects of the users that lead to unintentionally clicking on phishing links. Such actions may lead to installing tracking software, downloading malware or viruses, or stealing private, sensitive information, to list a few. Therefore, a system is needed that detects whether a click on a link is intentional or unintentional and, if unintentional, can then prevent it. This paper proposes a micro-behavioral accidental click detection system (ACDS) to prevent slip-based human error. A within-subject-based experiment was conducted with 20 participants to test the potential of the proposed system. The results reveal the statistical significance between the two cases of intentional vs. unintentional clicks using a smartphone. Random tree, random forest, and support vector machine classifiers were used, exhibiting 82.6%, 87.2%, and 91.6% accuracy in detecting unintentional clicks, respectively.

Measuring (online) word segmentation in adults and children

Since Saffran, Aslin and Newport (1996) showed that infants were sensitive to transitional probabilities between syllables after being exposed to a few minutes of fluent speech, there has been ample research on statistical learning. Word segmentation studies usually test learning by making use of “offline methods” such as forced-choice tasks. However, cognitive factors besides statistical learning possibly influence performance on those tasks. The goal of the present study was to improve a method for measuring word segmentation online. Click sounds were added to the speech stream, both between words and within words. Stronger expectations for the next syllable within words as opposed to between words were expected to result in slower detection of clicks within words, revealing sensitivity to word boundaries. Unexpectedly, we did not find evidence for learning in multiple groups of adults and child participants. We discuss possible methodological factors that could have influenced our results.

Separating overlapping echolocation: An updated method for estimating the number of echolocating animals in high background noise levels.

Much can be learned by investigating the click trains of odontocetes, including estimating the number of vocalizing animals and comparing the acoustic behavior of different individuals. Analyzing such information gathered from groups of echolocating animals in a natural environment is complicated by two main factors: overlapping echolocation produced by multiple animals at the same time, and varying levels of background noise. Starkhammar et al. [(2011a). Biol. Lett. 7(6), 836-839] described an algorithm that measures and compares the frequency spectra of individual clicks to identify groups of clicks produced by different individuals. This study presents an update to this click group separation algorithm that improves performance by comparing multiple click characteristics. There is a focus on reducing error when high background noise levels cause false click detection and recordings are of a limited frequency bandwidth, making the method applicable to a wide range of existing datasets. This method was successfully tested on recordings of free-swimming foraging dolphins with both low and high natural background noise levels. The algorithm can be adjusted via user-set parameters for application to recordings with varying sampling parameters and to species of varying click characteristics, allowing for estimates of the number of echolocating animals in free-swimming groups.

An experimental study of the detection of clicks in English

Abstract This experimental study sets out to determine whether people detect click sounds in American English. Recent research has documented the use of non-phonemic clicks in a variety of languages to fulfill a range of functions such as sequence management or signaling searches and different types of attitudinal stance. While these clicks are acoustically salient and have been reported to occur with a frequency of up to 14 per minute in British English, they have not been widely investigated until relatively recently. For this experiment, we designed video stimuli consisting of A and B pairs of approximately 10 seconds of speech, one with a click and the other with the click edited out. We gave 118 participants a questionnaire and asked if they could detect a difference between the pairs of videos. The results indicate that the majority of participants, between 79% and 86%, do not detect click sounds.