Automatic Detectors for Underwater Soundscape Measurements

Environmental impact regulations require that marine industrial operators quantify their contribution to underwater noise scenes. Automation of such assessments becomes feasible with the successful categorisation of sounds into broader classes based on source types – biological, anthropogenic and physical. Previous approaches to passive acoustic monitoring have mostly been limited to a few specific sources of interest. In this study, source-independent signal detectors are developed and a framework is presented for the automatic categorisation of underwater sounds into the aforementioned classes

Improve automatic detection of animal call sequences with temporal context

Funding: This work was supported by the US Office of Naval Research (grant no. N00014-17-1-2867).Many animals rely on long-form communication, in the form of songs, for vital functions such as mate attraction and territorial defence. We explored the prospect of improving automatic recognition performance by using the temporal context inherent in song. The ability to accurately detect sequences of calls has implications for conservation and biological studies. We show that the performance of a convolutional neural network (CNN), designed to detect song notes (calls) in short-duration audio segments, can be improved by combining it with a recurrent network designed to process sequences of learned representations from the CNN on a longer time scale. The combined system of independently trained CNN and long short-term memory (LSTM) network models exploits the temporal patterns between song notes. We demonstrate the technique using recordings of fin whale (Balaenoptera physalus) songs, which comprise patterned sequences of characteristic notes. We evaluated several variants of the CNN + LSTM network. Relative to the baseline CNN model, the CNN + LSTM models reduced performance variance, offering a 9-17% increase in area under the precision-recall curve and a 9-18% increase in peak F1-scores. These results show that the inclusion of temporal information may offer a valuable pathway for improving the automatic recognition and transcription of wildlife recordings.Publisher PDFPeer reviewe

Astrophysical S_{17}(0) factor from a measurement of d(7Be,8B)n reaction at E_{c.m.} = 4.5 MeV

Angular distribution measurements of $^2$H($^7$Be,$^7$Be)$^2$H and $^2$H($^7$Be,$^8$B)$n$ reactions at $E_{c.m.}\sim$~4.5 MeV were performed to extract the astrophysical $S_{17}(0)$ factor using the asymptotic normalization coefficient (ANC) method. For this purpose a pure, low emittance $^7$Be beam was separated from the primary $^7$Li beam by a recoil mass spectrometer operated in a novel mode. A beam stopper at 0$^{\circ}$ allowed the use of a higher $^7$Be beam intensity. Measurement of the elastic scattering in the entrance channel using kinematic coincidence, facilitated the determination of the optical model parameters needed for the analysis of the transfer data. The present measurement significantly reduces errors in the extracted $^7$Be(p,$\gamma$) cross section using the ANC method. We get $S_{17}$~(0)~=~20.7~$\pm$~2.4 eV~b.Comment: 15 pages including 3 eps figures, one figure removed and discussions updated. Version to appear in Physical Review

Humpback whale singing activity off the Goan coast in the Eastern Arabian Sea

<p>For over two decades, passive acoustic monitoring (PAM) methods have been successfully employed around the world for studying aquatic megafauna. PAM-driven studies in Indian waters have so far been relatively very scarce. Furthermore, cetacean populations inhabiting the north western Indian Ocean are far less studied than those in many other regions around the world. This work likely constitutes the first systematic study of the vocal repertoire of humpback whales (<i>Megaptera novaeangliae</i>) at a near-shore site along the western coast of India. Analysis of the observed vocalizations provides an insight into the behaviour of the species. This is significant as it assists in developing a better understanding of the habitat use of the non-migratory Arabian Sea humpback whale population. In contrast, other breeding populations such as those around the North Atlantic, South Pacific and Australia have been relatively well studied. Underwater passive acoustic data were collected during March 2017 using an autonomous logger at a shallow-water site off the eastern edge of Grande Island off the coast of Goa. Humpback whale vocalizations were found to occur over multiple days in the recordings. Time–frequency contours of individual units of vocalization were extracted with the aid of an automatic detection technique and the characteristics of the units were measured. Further, successive units were analysed for formation of phrases and themes. Reconstruction of putative songs from the identified units and themes was not possible due to the limitations imposed by the nature of data collection. Detailed analyses of units, phrases and themes are presented.</p

Deep learning algorithm outperforms experienced human observer at detection of blue whale D‐calls: a double‐observer analysis

Abstract An automated algorithm for passive acoustic detection of blue whale D‐calls was developed based on established deep learning methods for image recognition via the DenseNet architecture. The detector was trained on annotated acoustic recordings from the Antarctic, and performance of the detector was assessed by calculating precision and recall using a separate independent dataset also from the Antarctic. Detections from both the human analyst and automated detector were then inspected by an independent judge to identify any calls missed by either approach and to adjudicate whether the apparent false‐positive detections from the automated approach were actually true positives. A final performance assessment was conducted using double‐observer methods (via a closed‐population Huggins mark–recapture model) to assess the probability of detection of calls by both the human analyst and automated detector, based on the assumption of false‐positive‐free adjudicated detections. According to our double‐observer analysis, the automated detector showed superior performance with higher recall and fewer false positives than the original human analyst, and with performance similar to existing top automated detectors. To understand the performance of both detectors we inspected the time‐series and signal‐to‐noise ratio (SNR) of detections for the test dataset, and found that most of the advantages from the automated detector occurred at low and medium SNR

The underwater soundscape around Australia

The Australian marine soundscape exhibits a diversity of sounds, which can be grouped into biophony, geophony and anthrophony based on their sources. Animals from tiny shrimp, to lobsters, fish and seals, to the largest animals on Earth, blue whales, contribute to the Australian marine biophony. Wind, rain, surf, Antarctic ice break-up and marine earthquakes make up the geophony. Ship traffic, mineral and petroleum exploration and production, construction, defence exercises and commercial fishing add to the anthrophony. While underwater recorders have become affordable mainstream equipment, precise sound recording and analysis remain an art. Australia’s Integrated Marine Observing System (IMOS) consists of a network of oceanographic and remote sensors, including passive acoustic listening stations managed by the Centre for Marine Science & Technology, Curtin University, Perth. All of the acoustic recordings are freely available online. Long-term records up to a decade exist at some sites. The recordings provide an exciting window into the underwater world. We present examples of soundscapes from around Australia and discuss various aspects of soundscape recording, analysis and reporting—the to-dos and not-to-dos

Detection of alcohol in saliva for blood alcohol concentration using alcohol saliva strip test: A forensic aid

Context: Alcohol is a factor in many categories of injury. Alcohol intoxication is frequently associated with injuries from falls, fires, drowning, overdoses, physical and sexual abusements, occupational accidents, traffic accidents and domestic violence. In many instances, for forensic purpose, it may be necessary to establish whether the patients/subjects have consumed alcohol that would have been the reason for the injury/accidents. Combining rapidity and reliability, alcohol saliva strip test (AST) has been put forward for the detection of alcohol in saliva for blood alcohol concentration (BAC). In the present study, we have determined BAC by using AST. Aims and Objectives: The main objective of this study was to detect alcohol in saliva for BAC in alcoholics by using AST. Materials and Methods: Two socio-economic groups were selected for the present study where Group A consisted of 40 subjects from the local bar and Group B consisted of 40 subjects from an organized party. The subjects were selected randomly at the local bar and at the organized party who have consumed different forms of alcohol. ALCO-SCREEN 02 plastic strip with a reactive pad was used for the detection of presence of alcohol in saliva. Results: In the present study, 85% of subjects from Group A, i.e., at the local bar, demonstrated positive results of variable intensity with AST when compared to the subjects from Group B in the organized party which was only about 25%. Conclusion: The present study showed that AST, performed by using ALCO-SCREEN 02 plastic strip with a reactive pad, can detect the presence of 0.02% BAC or more that can be helpful for various purposes such as forensic, workplace, medical and research settings. The study also showed that amount, time period, concentration and quality of the alcohol intake can influence the BAC, which can be a contributory factor for many accidents, injuries and medical conditions

Neotropical forest soundscapes with call identifications for katydids

Insects are an integral part of terrestrial ecosystems, but while they are ubiquitous, they can be difficult to census. Passive acoustic recording can provide detailed information on the spatial and temporal distribution of sound producing insects. We placed recording devices in the forest canopy on Barro Colorado Island in Panamá and identified katydid calls in recordings to assess what species were present, in which seasons they were signaling, and how often they called. The focal recordings were collected at a height of 24 meters in two replicate sites, sampled three times per night across five months, spanning both wet and dry seasons. Katydid calls were commonly detected in recordings, but the call repetition rates of many species were quite low, consistent with findings from individual focal recordings. The recordings contained 6,789 calls with visible pulse structure. Of these, we identified 4,371 to species with the remainder representing calls that could not be identified to species. The identified calls corresponded to 24 species, with 15 of these species detected at both replicate sites. Katydid calls were detected throughout the night. Most species were detected at all three timepoints in the night, although some species called more just after dusk and just before dawn. The annotated dataset provided here serves as an archival sample of the species diversity and number of calls present in the forest canopy of Barro Colorado Island, Panama. These hand-annotated data will also be key for evaluating automated approaches to detecting and classifying insect calls. In changing forests and with potentially declining insect populations, consistent approaches for insect sampling will be key for generating interpretable and actionable data.Funding provided by: National Geographic SocietyCrossref Funder Registry ID: http://dx.doi.org/10.13039/100006363Award Number: NGS-57246T-1

Calling songs of Neotropical katydids (Orthoptera: Tettigoniidae) from Panama

Understanding the ecology and evolution of animal communication systems requires detailed data on signal structure and variation across species. Here, we describe the male acoustic signals of 50 species of Neotropical katydids (Orthoptera: Tettigoniidae) from Panama, with the goal of providing data and recordings for future research on katydid communication, evolution, ecology, and conservation. Male katydids were recorded individually using an ultrasound-sensitive microphone and high-sampling rate data acquisition board to capture both audible and ultrasonic components of calls. Calls varied enormously in duration, temporal patterning, peak frequency, and bandwidth both across and within subfamilies. We confirm previous studies showing that katydid species within the subfamily Pseudophyllinae produced short calls (<250 ms) at long intervals and we confirm that this is true for species in the subfamily Phaneropterinae as well. Species in the Conocephalinae, on the other hand, typically produced highly repetitive calls over longer periods of time. However, there were exceptions to this pattern, with a few species in the Conocephalinae producing very short calls at long intervals, and some species in the Phaneropterinae producing relatively long calls (1–6 s) or calling frequently. Our results also confirm previous studies showing a relationship between katydid size and the peak frequency of the call, with smaller katydids producing higher frequency calls, but the slope of this relationship differed with subfamily. We discuss the value of documenting the diversity in katydid calls for both basic studies on the ecology, evolution, and behavior of these species as well as the potential conservation benefits for bioacoustics monitoring programs

Estimation of katydid calling activity from soundscape recordings

Insects are an integral part of terrestrial ecosystems, but while they are ubiquitous, they can be difficult to census. Passive acoustic recording can provide detailed information on the spatial and temporal distribution of sound-producing insects. We placed recording devices in the forest canopy on Barro Colorado Island in Panamá and identified katydid calls in recordings to assess what species were present, in which seasons they were signaling, and how often they called. Soundscape recordings were collected at a height of 24 m in two replicate sites, sampled at three time-windows per night across five months, spanning both wet and dry seasons. Katydid calls were commonly detected in recordings, but the call repetition rates of many species were quite low, consistent with data from focal recordings of individual insects where calls were also repeated rarely. The soundscape recordings contained 6,789 calls with visible pulse structure. Of these calls, we identified 4,371 to species with the remainder representing calls that could not be identified to species. The identified calls corresponded to 24 species, with 15 of these species detected at both replicate sites. Katydid calls were detected throughout the night. Most species were detected at all three time points in the night, although some species called more just after dusk and just before dawn. The annotated dataset provided here serves as an archival sample of the species diversity and number of calls present in the forest canopy of Barro Colorado Island, Panama. These hand-annotated data will also be key for evaluating automated approaches to detecting and classifying insect calls. In changing forests and with declining insect populations, consistent approaches to insect sampling will be key for generating interpretable and actionable data