Abstract The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using ...vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the ‘Speak up and help beat coronavirus’ digital survey alongside demographic, symptom and self-reported respiratory condition data. Digital survey submissions were linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,565 of 72,999 participants and 24,105 of 25,706 positive cases. Respiratory symptoms were reported by 45.6% of participants. This dataset has additional potential uses for bioacoustics research, with 11.3% participants self-reporting asthma, and 27.2% with linked influenza PCR test results.
Different studies suggest some social calls could be used in fish identification if their specificity is unambiguously assessed. Sounds of different populations of piranhas Serrasalmus maculatus ...Kner, 1858 were recorded to determine their homogeneity between rivers inside a single basin (Araguari and Grande River, upper Paraná River basin) and between separated basins (Amazon and Paraná basins). All fish from the different populations produced sounds with similar acoustic features. Consequently, the populations were not discernible based on individual sound characteristics. This high homogeneity between sounds from different populations indicates their usefulness for conservation projects using passive acoustic monitoring in piranhas. Moreover, it supports the use of acoustic features as complementary key characteristics in taxonomic studies.
Traffic noise is one of the leading causes of reductions in animal abundances near roads. Acoustic masking of conspecific signals and adventitious cues is one mechanism that likely causes animals to ...abandon loud areas. However, masking effects can be difficult to document in situ and the effects of infrequent noise events may be impractical to study. Here, we present the Soundscapes model, a stochastic individual-based model that dynamically models the listening areas of animals searching for acoustic resources (“searchers"). The model also studies the masking effects of noise for human detections of the searchers. The model is set in a landscape adjacent to a road. Noise produced by vehicles traveling on that road is represented by calibrated spectra that vary with speed. Noise propagation is implemented using ISO-9613 procedures. We present demonstration simulations that quantify declines in searcher efficiency and human detection of searchers at relatively low traffic volumes, fewer than 50 vehicles per hour. Traffic noise is pervasive, and the Soundscapes model offers an extensible tool to study the effects of noise on bioacoustics monitoring, point-count surveys, the restorative value of natural soundscapes, and auditory performance in an ecological context.
The behavioral study of animals and especially avians, and the way of their immunization are highly needed to understand the environment in a better way. Automatically classifying bird species by ...their vocalization is of crucial relevance for the research of ornithologists and ecologists. It was observed that impartial survey information for songbird species is inherently challenging due to observer biases, habitat insurance biases, and logistical constraints. To get to the bottom of all the challenges, ecologists are trying a machine that let them decide the distribution and density of species, which are essential baseline facts for conservation. For this reason, the utilization of a network of unmanned aerial vehicles is introduced for monitoring and capturing the data of a wide variety of terrestrial and aquatic species. In this study, an edge-enabled drone network has been engineered that amalgamated with the mobile edge computing framework within the drone network and the machine learning models to predict the bird species. The experiment has been performed in two geographic regions. The research reported 98.2% and 96.9% accuracy of random forest classifier with the, 0.07 and 0.4 log loss by utilizing 1.4% of CPU and 329.14 Mb of buffer memory of the edge device with an execution time of 45 milliseconds.
•The work primarily focuses on the design of an edge enabled ecosystem for bird species identification.•A drone network has been engineered that amalgamated with the mobile edge computing framework.•Various machine learning models have been implemented to predict the bird species.•The research reported 98.2% and 96.9% accuracy of random forest classifier with the, 0.07 and 0.4 log loss.
Summary
Numerous studies over the past decade have reported correlations between elevated levels of anthropogenic noise and a rise in the minimum frequency of acoustic signals of animals living in ...noisy habitats. This pattern appears to be occurring globally, and higher pitched signals have been hypothesized to be adaptive changes that reduce masking by low‐frequency traffic noise. However, the sound analysis methods most often used in these studies are prone to measurement errors that can result in false positives. In addition, the commonly used method of measuring frequencies visually from spectrograms might also lead to observer‐expectancy biases that could exacerbate measurement errors.
We conducted an experiment to (i) quantify the size and type of errors that result from ‘eye‐balling’ frequency measurements with cursors placed manually on spectrograms of signals recorded in noise and no‐noise conditions, and (ii) to test whether observer expectations lead to significant errors in frequency measurements. We asked 54 volunteers, blind to the true intention of our study, to visually measure the minimum frequency of a variety of natural and synthesized bird sounds, recorded either in noise, or no‐noise conditions. Test subjects were either informed or uninformed about the hypothesized results of the measurements.
Our results demonstrate that inappropriate methodology in acoustic analysis can yield false positives with effect sizes as large, or even larger, than those reported in published studies. In addition to these measurement artefacts, psychological observer biases also led to false positives – when observers expected signals to have higher minimum frequencies in noise, they measured significantly higher minimum frequencies than uninformed observers, who had not been primed with any expectation.
The use of improper analysis methods in bioacoustics can lead to the publication of spurious results. We discuss alternative methods that yield unbiased frequency measures and we caution that it is imperative for researchers to familiarize themselves both with the functions and limitations of their sound analysis programmes. In addition, observer‐expectancy biases are a potential source of error not only in the field of bioacoustics, but in any situation where measurements can be influenced by human subjectivity.
The acoustic arms race between insectivorous bats and their invertebrate prey has led to the convergent evolution of ultrasound hearing in seven orders of nocturnal insects. Upon hearing the ...echolocation calls of an approaching bat such insects take defensive action. Here we document an unknown sense of ultrasound hearing and phonotactic flight behaviour in the neuropteran family Myrmeleontidae (antlions). The antlion
was presented with sound pulses at ultrasonic frequencies used by echolocating bats and its response thresholds in tethered flight determined. Behaviours included abdominal twitches, wing-flicks, brief pauses in flight and flight cessation. Such behaviours create erratic evasive flight manoeuvres in other eared insects, particularly mantids and lacewings. Antlions responded best to ultrasound between 60-80 kHz (75 dB peSPL at 80 kHz) showing response thresholds similar to the related lacewings (Neuroptera, Chrysopidae). Yet at lower ultrasonic frequencies (20-50 kHz) antlions were far less sensitive than lacewings. Based on calculated response distances we conclude that antlions respond only after having been detected by bats rather than using early evasive flights. We argue that the high response threshold for low frequency ultrasound is adaptive for an insect that is mainly active close to and within vegetation, because a behavioural response to the lower ultrasonic frequencies used by high-flying bats would result in evasive action in the absence of actual predation risk.
A new subspecies, Tibicina quadrisignata pilleti Puissant ssp. nov. is described from Morocco. This cicada is similar to Tibicina quadrisignata (Hagen, 1855) but differs mainly in the body color of ...the males and some acoustic parameters, notably in the courtship song. A complete pipeline is developed for detailed analysis of the morphology and acoustic behavior of the two Tibicina taxa currently known in Morocco, i.e. Tibicinaquadrisignata (Hagen, 1855) pilleti Puissant ssp. nov. and Tibicina maldesi Boulard, 1981. Biology, ecology and distribution are presented for the first time for both taxa. Tibicinamaldesi emits an abnormally high carrier frequency relative to its size. It could be an adaptation to avoid acoustic interference with a sympatric Tibicina species yet to be discovered or extinct.
Migratory organisms face many challenges as they travel to take advantage of changing resources, exhibiting a variety of strategies to successfully move between locations. Birds are a model taxa for ...understanding migratory systems, relying on a multitude of cues and showing diverse behaviours, one of which is their propensity to give calls during migratory flight. However, this behaviour is understudied and may have implications in orientation, navigation and migration monitoring. Because a variety of migratory behaviours differ among and within species as a function of age, sex and body condition, we evaluated whether flight-calling behaviour was related to any of these variables. We studied flight-calling behaviour in four species of passage migrant warblers in captivity during autumn migration at Powdermill Avian Research Center (Rector, PA, U.S.A.) and Braddock Bay Bird Observatory (Greece, NY, U.S.A.). Our results demonstrate extensive variation in flight-calling behaviour by migrant warblers. When all species were combined, young birds were more likely to call and call at a higher rate than adults. All species-specific models were consistent in suggesting a higher propensity and rate of calling by young birds, although the black-throated blue warbler, Setophaga caerulescens, was the only species-specific model with significant age-related differences. When all species were combined, sex did not have a significant effect on either propensity or rate of calling. The effect of sex was inconsistent across species, and only magnolia warbler, Setophaga magnolia, males were significantly more likely to respond to calls than females. Surprisingly, body condition did not significantly affect the propensity or rate of calling. While our results reflect the complexity of flight-calling behaviour, our finding that young migrants consistently gave more calls has broad utility, particularly in quantitative migration monitoring. These results demonstrate the need for additional study to determine the selective forces influencing flight-calling behaviour.
•First evaluation of flight-calling behaviour in relation to age, sex and condition.•Young warblers were more likely to flight-call and call at a higher rate than adults.•Sex did not have a significant effect on flight-calling behaviour in most species.•Body condition had inconsistent effects on propensity and rate of flight calling.•Flight-calling behaviour varied extensively at species and individual levels.
A new species of the genus
,
, is described in this study based on morphological, molecular, and bioacoustic data. The species was discovered in the Shiwandashan National Nature Reserve in Shangsi ...County, Guangxi, China. Phylogenetically,
is closely related to
. However, there are distinct morphological differences between
and
, as well as three other sympatric species (
,
, and
). These differences include body size (SVL 30.5-32.5 mm in males; 38.7-41.8 mm in females in the new species vs 25.5-28.0 mm in males, 31.5-35.0 mm in females in
), the absence of brown spots on the ventral surface (vs chest and belly creamy white with many scattered brown spots in
), 1/3 toe webbing and wide toe lateral fringes (vs no toe webbing and no lateral fringes in
), and distinct dermal ridges under toes (vs absent in
). Furthermore, the dominant vocal frequencies of the new species range from 7.3 to 8.3 kHz, which is unique compared to other
species and represents the highest dominant frequencies ever recorded. The Shiwandashan National Nature Reserve is now home to four known sympatric species of
.
Occupancy modeling is used to evaluate avian distributions and habitat associations, yet it typically requires extensive survey effort because a minimum of 3 repeat samples are required for accurate ...parameter estimation. Autonomous recording units (ARUs) can reduce the need for surveyors on-site, yet their utility was limited by hardware costs and the time required to manually annotate recordings. Software that identifies bird vocalizations may reduce the expert time needed if classification is sufficiently accurate. We assessed the performance of BirdNET—an automated classifier capable of identifying vocalizations from >900 North American and European bird species—by comparing automated to manual annotations of recordings of 13 breeding bird species collected in northwestern California. We compared the parameter estimates of occupancy models evaluating habitat associations supplied with manually annotated data (9-min recording segments) to output from models supplied with BirdNET detections. We used 3 sets of BirdNET output to evaluate the duration of automatic annotation needed to approach manually annotated model parameter estimates: 9-min, 87-min, and 87-min of high-confidence detections. We incorporated 100 3-s manually validated BirdNET detections per species to estimate true and false positive rates within an occupancy model. BirdNET correctly identified 90% and 65% of the bird species a human detected when data were restricted to detections exceeding a low or high confidence score threshold, respectively. Occupancy estimates, including habitat associations, were similar regardless of method. Precision (proportion of true positives to all detections) was >0.70 for 9 of 13 species, and a low of 0.29. However, processing of longer recordings was needed to rival manually annotated data. We conclude that BirdNET is suitable for annotating multispecies recordings for occupancy modeling when extended recording durations are used. Together, ARUs and BirdNET may benefit monitoring and, ultimately, conservation of bird populations by greatly increasing monitoring opportunities. LAY SUMMARY Occupancy modeling provides valuable information for understanding bird distributions, but often requires extensive survey effort. Autonomous recording units (ARUs) produce vast amounts of data, yet manually identifying birds on recordings is time-consuming. We evaluated the performance of an automated bird sound classifier, BirdNET, by comparing occupancy models that used manually and BirdNET-annotated data for 13 species in northwestern California, USA. We manually identified bird species heard during 9-min recordings at 34 sites, and used BirdNET to identify birds during 9–260-min recordings from each site. We also manually verified 100 BirdNET detections for each species. BirdNET correctly identified most bird species detected during manual bird identification when data were restricted respectively to nearly all (90% correct) or only high confidence (65% correct) detections. Habitat associations were similar across all models. We conclude that BirdNET is a useful tool for automatic annotation of bird vocalization data needed to model bird presence or absence.
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