Waveguide invariant in a gradual range- and azimuth-varying waveguide

Experimental data indicate that in a sloped area, the value of β abruptly changes before and after a given source arrives at the closest point of approach to the hydrophone, which has not been previously reported. The adiabatic approximation is employed to explain the above abrupt change in β, and it is found that the azimuthal variance in the sound path is the reason for this phenomenon. Simulations are performed to confirm the model and experimental data, and perfect agreement is achieved. This work suggests that β should be carefully set in related applications in a sloped area.

Cancellation of dolphin sonar clicks in a communication signal based on adaptive time reversal processing

In long-range underwater communication, conventional time reversal processing (CTRP) is used to mitigate the distortion caused by multipath and temporal spreading. However, signals produced by marine animals can contaminate communication signals. Impulsive signals, such as dolphin sonar clicks, have wide bandwidths and short pulse durations, making it difficult to isolate the communication signal. This letter proposes a method to cancel these sounds by estimating the Green's function of marine animal clicks and applying adaptive time reversal processing (ATRP). The effectiveness of the click nulling was verified by comparing the performances of CTRP and ATRP with seagoing experimental data.

Infrasound direction of arrival determination using a balloon-borne aeroseismometer

Free-floating balloons are an emerging platform for infrasound recording, but they cannot host arrays sufficiently wide for multi-sensor acoustic direction finding techniques. Because infrasound waves are longitudinal, the balloon motion in response to acoustic loading can be used to determine the signal azimuth. This technique, called "aeroseismometry," permits sparse balloon-borne networks to geolocate acoustic sources. This is demonstrated by using an aeroseismometer on a stratospheric balloon to measure the direction of arrival of acoustic waves from successive ground chemical explosions. A geolocation algorithm adapted from hydroacoustics is then used to calculate the location of the explosions.

Echolocation click discrimination for three killer whale ecotypes in the Northeastern Pacific

Three killer whale ecotypes are found in the Northeastern Pacific: residents, transients, and offshores. These ecotypes can be discriminated in passive acoustic data based on distinct pulsed call repertoires. Killer whale acoustic encounters for which ecotypes were assigned based on pulsed call matching were used to characterize the ecotype-specific echolocation clicks. Recordings were made using seafloor-mounted sensors at shallow (∼120 m) and deep (∼1400 m) monitoring locations off the coast of Washington state. All ecotypes' echolocation clicks were characterized by energy peaks between 12 and 19 kHz, however, resident clicks featured sub peaks at 13.7 and 18.8 kHz, while offshore clicks had a single peak at 14.3 kHz. Transient clicks were rare and were characterized by lower peak frequencies (12.8 kHz). Modal inter-click intervals (ICIs) were consistent but indistinguishable for resident and offshore killer whale encounters at the shallow site (0.21-0.22 s). Offshore ICIs were longer and more variable at the deep site, and no modal ICI was apparent for the transient ecotype. Resident and offshore killer whale ecotype may be identified and distinguished in large passive acoustic datasets based on properties of their echolocation clicks, however, transient echolocation may be unsuitable in isolation as a cue for monitoring applications.

Discriminating and classifying odontocete echolocation clicks in the Hawaiian Islands using machine learning methods

Passive acoustic monitoring (PAM) has proven a powerful tool for the study of marine mammals, allowing for documentation of biologically relevant factors such as movement patterns or animal behaviors while remaining largely non-invasive and cost effective. From 2008-2019, a set of PAM recordings covering the frequency band of most toothed whale (odontocete) echolocation clicks were collected at sites off the islands of Hawai'i, Kaua'i, and Pearl and Hermes Reef. However, due to the size of this dataset and the complexity of species-level acoustic classification, multi-year, multi-species analyses had not yet been completed. This study shows how a machine learning toolkit can effectively mitigate this problem by detecting and classifying echolocation clicks using a combination of unsupervised clustering methods and human-mediated analyses. Using these methods, it was possible to distill ten unique echolocation click 'types' attributable to regional odontocetes at the genus or species level. In one case, auxiliary sightings and recordings were used to attribute a new click type to the rough-toothed dolphin, Steno bredanensis. Types defined by clustering were then used as input classes in a neural-network based classifier, which was trained, tested, and evaluated on 5-minute binned data segments. Network precision was variable, with lower precision occurring most notably for false killer whales, Pseudorca crassidens, across all sites (35-76%). However, accuracy and recall were high (>96% and >75%, respectively) in all cases except for one type of short-finned pilot whale, Globicephala macrorhynchus, call class at Kaua'i and Pearl and Hermes Reef (recall >66%). These results emphasize the utility of machine learning in analysis of large PAM datasets. The classifier and timeseries developed here will facilitate further analyses of spatiotemporal patterns of included toothed whales. Broader application of these methods may improve the efficiency of global multi-species PAM data processing for echolocation clicks, which is needed as these datasets continue to grow.

The sound of one frog calling: The bullfrog's reactions to acoustic stimuli

The Reflections series takes a look back on historical articles from The Journal of the Acoustical Society of America that have had a significant impact on the science and practice of acoustics.

An overview of ambient sound using Ocean Observatories Initiative hydrophones

The Ocean Observatories Initiative (OOI) sensor network provides a unique opportunity to study ambient sound in the north-east Pacific Ocean. The OOI sensor network has five low frequency (Fs = 200 Hz) and six broadband (Fs = 64 kHz) hydrophones that have been recording ambient sound since 2015. In this paper, we analyze acoustic data from 2015 to 2020 to identify prominent features that are present in the OOI acoustic dataset. Notable features in the acoustic dataset that are highlighted in this paper include volcanic and seismic activity, rain and wind noise, marine mammal vocalizations, and anthropogenic sound, such as shipping noise. For all low frequency hydrophones and four of the six broadband hydrophones, we will present long-term spectrograms, median time-series trends for different spectral bands, and different statistical metrics about the acoustic environment. We find that 6-yr acoustic trends vary, depending on the location of the hydrophone and the spectral band that is observed. Over the course of six years, increases in spectral levels are seen in some locations and spectral bands, while decreases are seen in other locations and spectral bands. Last, we discuss future areas of research to which the OOI dataset lends itself.

Effects of duty cycles on passive acoustic monitoring of southern resident killer whale (Orcinus orca) occurrence and behavior

Long-term passive acoustic monitoring of cetaceans is frequently limited by the data storage capacity and battery life of the recording system. Duty cycles are a mechanism for subsampling during the recording process that facilitates long-term passive acoustic studies. While duty cycles are often used, there has been little investigation on the impact that this approach has on the ability to answer questions about a species' behavior and occurrence. In this study, the effects of duty cycling on the acoustic detection of southern resident killer whales (SRKW) (Orcinus orca) were investigated. Continuous acoustic data were subsampled to create 288 subsampled datasets with cycle lengths from 5 to 180 min and listening proportions from 1% to 67%. Duty cycles had little effect on the detection of the daily presence of SRKW, especially when using cycle lengths of less than an hour. However, cycle lengths of 15-30 min and listening proportions of at least 33% were required to accurately calculate durations of acoustic bouts and identify those bouts to ecotype. These results show that the optimal duty cycle depends on the scale of the research question and provide a framework for quantitative analysis of duty cycles for other marine species.

Extraction of Premature Newborns' Spontaneous Cries in the Real Context of Neonatal Intensive Care Units

Cry analysis is an important tool to evaluate the development of preterm infants. However, the context of Neonatal Intensive Care Units is challenging, since a wide variety of sounds can occur (e.g., alarms and adult voices). In this paper, a method to extract cries is proposed. It is based on an initial segmentation between silence and sound events, followed by feature extraction on the resulting audio segments and a cry and non-cry classification. A database of 198 cry events coming from 21 newborns and 439 non-cry events was created. Then, a set of features—including Mel-Frequency Cepstral Coefficients—issued from principal component analysis, was computed to describe each audio segment. For the first time in cry analysis, noise was handled using harmonic plus noise analysis. Several machine learning models have been compared. The K-Nearest Neighbours approach showed the best results with a precision of 92.9%. To test the approach in a monitoring application, 412 h of recordings were automatically processed. The cries automatically selected were replayed and a precision of 92.2% was obtained. The impact of errors on the fundamental frequency characterisation was also studied. Results show that despite a difficult context, automatic cry extraction for non-invasive monitoring of vocal development of preterm infants is achievable.

Potential impacts from simulated vessel noise and sonar on commercially important invertebrates

Human usage of coastal water bodies continues to increase and many invertebrates face a broad suite of anthropogenic stressors (e.g., warming, pollution, acidification, fishing pressure). Underwater sound is a stressor that continues to increase in coastal areas, but the potential impact on invertebrates is not well understood. In addition to masking natural sound cues which may be important for behavioral interactions, there is a small but increasing body of scientific literature indicating sublethal physiological stress may occur in invertebrates exposed to high levels of underwater sound, particularly low frequency sounds such as vessel traffic, construction noise, and some types of sonar. Juvenile and sub-adult blue crabs (Callinectes sapidus) and American lobsters (Homarus americanus) were exposed to simulated low-frequency vessel noise (a signal was low-pass filtered below 1 kHz to ensure low-frequency content only) and mid-frequency sonar (a 1-s 1.67 kHz continuous wave pulse followed by a 2.5 to 4.0 kHz 1-s linear frequency modulated chirp) and behavioral response (the animal's activity level) was quantified during and after exposure using EthoVision XT™ from overhead video recordings. Source noise was quantified by particle acceleration and pressure. Physiological response to the insults (stress and recovery) were also quantified by measuring changes in hemolymph heat shock protein (HSP27) and glucose over 7 days post-exposure. In general, physiological indicators returned to baseline levels within approximately 48 h, and no observable difference in mortality between treatment and control animals was detected. However, there was a consistent amplified hemolymph glucose signal present 7 days after exposure for those animals exposed to mid-frequency sound and there were changes to C. sapidus competitive behavior within 24 h of exposure to sound. These results stress the importance of considering the impacts of underwater sound among the suite of stressors facing marine and estuarine invertebrates, and in the discussion of management actions such as protected areas, impact assessments, and marine spatial planning efforts.

Acoustic characteristics from an in-water down-the-hole pile drilling activity

Sound generated by pile installation using a down-the-hole (DTH) hammer is not well documented and differs in character from sound generated by conventional impact and vibratory pile driving. This paper describes underwater acoustic characteristics from DTH pile drilling during the installation of 0.84-m shafts within 1.22-m steel piles in Ketchikan, Alaska. The median single-strike sound exposure levels were 138 and 142 dB re 1 μPa²s at 10 m for each of the two piles, with cumulative sound exposure levels of 185 and 193 dB re 1 μPa²s at 10 m, respectively. The sound levels measured at Ketchikan were significantly lower than previous studies, and the sound was determined to be non-impulsive in this study as compared to impulsive in previous studies. These differences likely result from the DTH hammer not making direct contact with the pile, as had been the case in previous studies. Therefore, we suggest using the term DTH pile drilling to distinguish from DTH pile driving when the hammer strikes the pile. Further research is needed to investigate DTH piling techniques and associated sound-generating mechanisms and to differentiate the various types of sound emitted, which has important implications for the underwater sound regulatory community.

Multi-target 2D tracking method for singing humpback whales using vector sensors

Acoustic vector sensors allow estimating the direction of travel of an acoustic wave at a single point by measuring both acoustic pressure and particle motion on orthogonal axes. In a two-dimensional plane, the location of an acoustic source can thus be determined by triangulation using the estimated azimuths from at least two vector sensors. However, when tracking multiple acoustic sources simultaneously, it becomes challenging to identify and link sequences of azimuthal measurements between sensors to their respective sources. This work illustrates how two-dimensional vector sensors, deployed off the coast of western Maui, can be used to generate azimuthal tracks from individual humpback whales singing simultaneously. Incorporating acoustic transport velocity estimates into the processing generates high-quality azimuthal tracks that can be linked between sensors by cross-correlating features of their respective azigrams, a particular time-frequency representation of sound directionality. Once the correct azimuthal track associations have been made between instruments, subsequent localization and tracking in latitude and longitude of simultaneous whales can be achieved using a minimum of two vector sensors. Two-dimensional tracks and positional uncertainties of six singing whales are presented, along with swimming speed estimates derived from a high-quality track.

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.

Acoustic traits of bat-pollinated flowers compared to flowers of other pollination syndromes and their echo-based classification using convolutional neural networks

Bat-pollinated flowers have to attract their pollinators in absence of light and therefore some species developed specialized echoic floral parts. These parts are usually concave shaped and act like acoustic retroreflectors making the flowers acoustically conspicuous to the bats. Acoustic plant specializations only have been described for two bat-pollinated species in the Neotropics and one other bat-dependent plant in South East Asia. However, it remains unclear whether other bat-pollinated plant species also show acoustic adaptations. Moreover, acoustic traits have never been compared between bat-pollinated flowers and flowers belonging to other pollination syndromes. To investigate acoustic traits of bat-pollinated flowers we recorded a dataset of 32320 flower echoes, collected from 168 individual flowers belonging to 12 different species. 6 of these species were pollinated by bats and 6 species were pollinated by insects or hummingbirds. We analyzed the spectral target strength of the flowers and trained a convolutional neural network (CNN) on the spectrograms of the flower echoes. We found that bat-pollinated flowers have a significantly higher echo target strength, independent of their size, and differ in their morphology, specifically in the lower variance of their morphological features. We found that a good classification accuracy by our CNN (up to 84%) can be achieved with only one echo/spectrogram to classify the 12 different plant species, both bat-pollinated and otherwise, with bat-pollinated flowers being easier to classify. The higher classification performance of bat-pollinated flowers can be explained by the lower variance of their morphology.

Baseline assessment of ocean ambient noise in the western Clarion Clipperton Zone, Pacific Ocean

Ocean noise in the western Clarion Clipperton Zone, Pacific Ocean was recorded for 5 min every hour during 2017 and 2018, at a depth of 300 m. The monthly and hourly mean spectrum levels in the 20-1000 Hz band were calculated, along with their skewness, kurtosis, percentile distributions, and spectral probability densities. The high noise levels at low frequencies generated from distant shipping and vocalizations of whales were found to range between 70 and 100 dB (<100 Hz) and 64-93 dB (100-200 Hz), respectively. The noise levels at high frequencies (>200 Hz), which are typically dominated by wind, were found to be low, ranging from 53 to 75 dB. At frequencies above 200 Hz, noise levels in winter were approximately 5 dB higher than those in summer, consistent with the seasonal variations in wind speed. Fin whales, blue whales, and fishes also potentially contributed to variations in the baseline of ambient noise.

Anthropogenic sound field and noise mapping in an Arctic fjord during summer

Sound Pressure Levels were recorded using an Automated Noise Measurement System, during July 2015 - April 2016 at the Kongsfjorden, Arctic. The fjord houses the NyAlesund port and has many vessels plying during summer, which contributes to anthropogenic noise. Spectral distribution and average sound level at 1/3-octave band from 63 Hz to 5000 Hz has been analyzed and correlated with Automatic Identification System marine traffic data. The radiated acoustic field from vessel transits has been predicted through source level modeling for different category vessels. Further, an acoustic propagation model MMPE based on Parabolic Equation method has been used to evaluate range dependent propagation along the fjord and Transmission Loss estimates have been calculated for upslope and down slope cases. Noise due to shipping has been estimated using Source-Path-Receiver Model using Propagation Loss model estimates, Sound Pressure Level, and Source Level predictions. Noise maps with level contours are generated for shipping, depicting the maximum sound levels for the Kongsfjorden.

Data driven source localization using a library of nearby shipping sources of opportunity

A library of broadband (100-1000 Hz) channel impulse responses (CIRs) estimated between a short bottom-mounted vertical line array (VLA) in the Santa Barbara channel and selected locations along the tracks of 27 isolated transiting ships, cumulated over nine days, is constructed using the ray-based blind deconvolution algorithm. Treating this CIR library either as data-derived replica for broadband matched-field processing (MFP) or training data for machine learning yields comparable ranging accuracy (∼50 m) for nearby vessels up to 3.2 km for both methods. Using model-based replica of the direct path only computed for an average sound-speed profile comparatively yields∼110 m ranging accuracy.

Solving one-dimensional acoustic systems using the impedance translation theorem and equivalent circuits: A graduate level homework assignment

The natural frequency resonances and sound radiation from one-dimensional acoustic systems are of great interest in the study of musical instruments, human vocal tract effects on speech, automotive exhaust pipes, duct systems for temperature control in buildings, and more. The impedance translation theorem is an approach that may be used to solve for the input impedance and therefore the resonance frequencies of one-dimensional systems. Equivalent circuits offer another approach to solving one-dimensional systems, though with equivalent circuits you can also solve for the response at any location in the system, including the radiated sound pressure. At Brigham Young University, there are two graduate level courses that teach these two techniques. One of the most challenging and memorable homework assignments from these courses is based on using one of these techniques to analyze a particular acoustic system and compare its response with the real thing. This paper discusses the basics of these two techniques and applies them to an analysis of phonemes produced by altering the human vocal tract. Details about the homework assignments are also given.

Echolocation variability of franciscana dolphins (Pontoporia blainvillei) between estuarine and open-sea habitats, with insights into foraging patterns

Environmental and ecological factors can trigger changes in the acoustic repertoire of cetaceans. This study documents the first use of a well-established passive acoustic monitoring device (C-POD) to analyze echolocation sounds and behavior of franciscana dolphins in different habitats: estuary [Babitonga Bay (BB)] and open sea [Itapirubá Beach (IB)]. A total of 10 924 click trains were recorded in BB and 6 093 in IB. An inter-click interval < 10 ms (so called "feeding buzzes") was used as a proxy for foraging activity. The main difference in the acoustic parameters between the two habitats was related to the frequency spectrum, with higher maximum and lower modal and minimum click frequencies in BB, and a train frequency range of 17 kHz, against 10 kHz in IB. Also, the click emission rate (clicks/s) was almost 20% higher in BB. Both studied habitats showed a high proportion of feeding buzzes (BB = 68%; IB = 58%), but with a higher probability of occurrence in BB (p < 0.001) and at night (p < 0.001) in both habitats. The C-PODs showed great potential to monitor occurrence, bioacoustics parameters, and echolocation behavior of franciscana dolphins. Longer-term temporal and spatial monitoring are necessary for elucidating several issues raised in this study.

Tracking time differences of arrivals of multiple sound sources in the presence of clutter and missed detections

Acoustic line transect surveys are often used in combination with visual methods to estimate the abundance of marine mammal populations. These surveys typically use towed linear hydrophone arrays and estimate the time differences of arrival (TDOAs) of the signal of interest between the pairs of hydrophones. The signal source TDOAs or bearings are then tracked through time to estimate the animal position, often manually. The process of estimating TDOAs from data and tracking them through time can be especially challenging in the presence of multiple acoustically active sources, missed detections, and clutter (false TDOAs). This study proposes a multi-target tracking method to automate TDOA tracking. The problem formulation is based on the Gaussian mixture probability hypothesis density filter and includes multiple sources, source appearance and disappearance, missed detections, and false alarms. It is shown that by using an extended measurement model and combining measurements from broadband echolocation clicks and narrowband whistles, more information can be extracted from the acoustic encounters. The method is demonstrated on false killer whale (Pseudorca crassidens) recordings from Hawaiian waters.

Unsupervised repetition enables rapid perceptual learning

This study examined how listeners disambiguate an auditory scene comprising multiple competing unknown sources and determine a salient source. Experiment 1 replicated findings from McDermott, Wrobleski, and Oxenham. [(2011). Proc. Natl. Acad. Sci. U. S. A. 108(3), 1188-1193] using a multivariate Gaussian model to generate mixtures of two novel sounds. The results showed that listeners were unable to identify either sound in the mixture despite repeated exposure unless one sound was repeated several times while being mixed with a different distractor each time. The results support the idea that repetition provides a basis for segregating a single source from competing novel sounds. In subsequent experiments, the previous identification task was extended to a recognition task and the results were modeled. To confirm the repetition benefit, experiment 2 asked listeners to recognize a temporal ramp in either a repeating sound or non-repeating sounds. The results showed that perceptual salience of the repeating sound allowed robust recognition of its temporal ramp, whereas similar features were ignored in the non-repeating sounds. The response from two neural models of learning, generalized Hebbian learning and anti-Hebbian learning, were compared with the human listener results from experiment 2. The Hebbian network showed a similar response pattern as for the listener results, whereas the opposite pattern was observed for the anti-Hebbian output.

Seasonality and social factors, but not noise pollution, influence the song characteristics of two leaf warbler species

Changes in the acoustic signalling of animals occupying urban ecosystems is often associated with the masking effects of noise pollution, but the way in which they respond to noise pollution is not straightforward. An increasing number of studies indicate that responses can be case specific, and some species have been found to respond differently to high levels of natural versus anthropogenic noise, as well as different levels of the latter. While the perception of noise between species may vary with its source, amplitude and temporal features, some species may possess broader environmental tolerance to noise pollution, as they use higher frequency vocalizations that are less masked by low-frequency urban noise. In this study, we explored the song variation of two closely related leaf warblers, the Common Chiffchaff Phylloscopus collybita and the Willow Warbler Phylloscopus trochilus, inhabiting urban green spaces and nonurban forests. The main goal of our study was to evaluate the impact of moderate levels of noise pollution on the songs of species which use higher frequency vocalizations and large frequency bandwidth. Previous studies found that the Common Chiffchaff modified their song in response to intense noise pollution, while no such data is available for the Willow Warbler. However, the majority of urban green spaces, which serve as wildlife hot spots in urban environments are usually polluted with moderate noise levels, which may not mask the acoustic signals of species that communicate with higher frequency. We analysed the spectral and temporal song parameters of both warblers and described the ambient noise present in males’ territories. Additionally, we looked at the social and seasonal aspects of bird song, since there is more than just noise in urban ecosystems which may affect acoustic communication. We found no evidence for noise-related bird song divergence in either species, however, we showed that social factors, time of day and season influence certain Common Chiffchaff and Willow Warbler song characteristics. Lack of noise-related bird song divergence may be due to the relatively low variation in its amplitude or other noise features present within the song frequency range of the studied species. Similar results have previously been shown for a few songbird species inhabiting urban ecosystems. Although in many cases such results remain in the shadow of the positive ones, they all contribute to a better understanding of animal communication in urban ecosystems.

Seal bomb explosion sound source characterization

Small explosive charges, called seal bombs, used by commercial fisheries to deter marine mammals from depredation and accidental bycatch during fishing operations, produce high level sounds that may negatively impact nearby animals. Seal bombs were exploded underwater and recorded at various ranges with a calibrated hydrophone to characterize the pulse waveforms and to provide appropriate propagation loss models for source level (SL) estimates. Waveform refraction became important at about 1500 m slant range with approximately spherical spreading losses observed at shorter ranges. The SL for seal bombs was estimated to be 233 dB re 1 μPa m; however, for impulses such as explosions, better metrics integrate over the pulse duration, accounting for the total energy in the pulse, including source pressure impulse, estimated as 193 Pa m s, and sound exposure source level, estimated as 197 dB re 1 μPa² m² s over a 2 ms window. Accounting for the whole 100 ms waveform, including the bubble pulses and sea surface reflections, sound exposure source level was 203 dB re 1 μPa² m² s. Furthermore, integrating the energy over an entire event period of multiple explosions (i.e., cumulative sound exposure level) should be considered when evaluating impact.

Individual Differences in the Adoption of Sound Change

It is still unclear whether an individual's adoption of on-going sound change starts in production or in perception, and what the time course of the adoption of sound change is in adult speakers. These issues are investigated by means of a large-scale (106 participants) laboratory study of an on-going vowel shift in Dutch. The shift involves the tense mid vowels /eː,øː,oː/, which are changing into phonologically conditioned upgliding diphthongs, and the original diphthongs /εi,œy,ɔu/, whose nuclei are lowering. These changes are regionally stratified: they have all but completed in the Netherlands, but have not affected the variety of Dutch spoken in neighboring Belgium. The study compares production (word-list reading) and perception (rhyme decision) data from control groups from each country to those of 18 "sociolinguistic migrants": Belgian individuals who moved to the Netherlands years ago. Data are analyzed using mixed-effects models, considering not just the group level, but also individual differences. Production results show that at the group level, the migrant group is in between the two control groups, but at the individual level it becomes apparent that some migrants have adopted the Netherlandic norms, but others have not. Perception results are similar to the production results at the group level. Individual-level results do not provide a clear picture for the perception data, but the individual differences in perception correlate with those in production. The results agree with and extend previous findings on the role of individual differences in the individual adoption and eventual community propagation of on-going sound change.

Description of the first acoustic recording of spinner dolphins (Stenella longirostris) from the northern straits of Malacca, Malaysia (L)

Relatively little is known about spinner dolphins in Malaysian waters and the wider Southeast Asian region. This note represents the first known acoustic recording of the species sighted opportunistically in the northern Straits of Malacca. Over a brief 20 min sighting, 46 whistles were recorded and four tonal types were detected, with 54.4% being upsweep whistles. The whistle duration ranged from 36 to 977 ms and the frequency ranged from 6.6 to 23.8 kHz. Fifty-seven click trains with a mean interclick interval of 41.5 ± 19.3 ms were detected. These findings provide a baseline for future regional acoustic research on this species.

FPM-β: A method for waveguide invariant estimation using one-dimensional broadband acoustic intensity

Based on the finiteness of normal modes that can propagate over long distances in an ocean waveguide, a waveguide invariant estimation method by combining β-warping transform operator and singular value decomposition is proposed in this letter. This method only needs to process the one-dimensional broadband acoustic intensity spectrum received by a single hydrophone and does not require any prior information of marine environmental parameters. Simulations and experiment verify the effectiveness of the method.

Mean absorption estimation from room impulse responses using virtually supervised learning

In the context of building acoustics and the acoustic diagnosis of an existing room, it introduces and investigates a new approach to estimate the mean absorption coefficients solely from a room impulse response (RIR). This inverse problem is tackled via virtually supervised learning, namely, the RIR-to-absorption mapping is implicitly learned by regression on a simulated dataset using artificial neural networks. Simple models based on well-understood architectures are the focus of this work. The critical choices of geometric, acoustic, and simulation parameters, which are used to train the models, are extensively discussed and studied while keeping in mind the conditions that are representative of the field of building acoustics. Estimation errors from the learned neural models are compared to those obtained with classical formulas that require knowledge of the room's geometry and reverberation times. Extensive comparisons made on a variety of simulated test sets highlight different conditions under which the learned models can overcome the well-known limitations of the diffuse sound field hypothesis underlying these formulas. Results obtained on real RIRs measured in an acoustically configurable room show that at 1 kHz and above, the proposed approach performs comparably to classical models when reverberation times can be reliably estimated and continues to work even when they cannot.

Analyzing the vocal tract characteristics for out-of-breath speech

In this work, vocal tract characteristic changes under the out-of-breath condition are explored. Speaking under the influence of physical exercise is called out-of-breath speech. The change in breathing pattern results in perceptual changes in the produced sound. For vocal tract, the first four formants show a lowering in their average frequency. The bandwidths B_F1 and B_F2 widen, whereas the other two get narrowed. The change in bandwidth is small for the last three. For a speaker, the change in frequency and bandwidth may not be uniform across formants. Subband analysis is carried out around formants for comparing the variation of the vocal tract with the source. A vocal tract adaptive empirical wavelet transform is used for extracting formant specific subbands from speech and source. The support vector machine performs the subband-based binary classification between the normal and out-of-breath speech. For all speakers, it shows an F1-score improvement of 4% over speech subbands. Similarly, a performance improvement of 5% can be seen for both male and female speakers. Furthermore, the misclassification amount is less for source compared to speech. These results suggest that physical exercise influences the source more than the vocal tract.

Underwater sound levels in the Canadian Arctic, 2014-2019

The Arctic has been a refuge from anthropogenic underwater noise; however, climate change has caused summer sea ice to diminish, allowing for unprecedented access and the potential for increased underwater noise. Baseline underwater sound levels must be quantified to monitor future changes and manage underwater noise in the Arctic. We analyzed 39 passive acoustic datasets collected throughout the Canadian Arctic from 2014 to 2019 using statistical models to examine spatial and temporal trends in daily mean sound pressure levels (SPL) and quantify environmental and anthropogenic drivers of SPL. SPL (50-1000 Hz) ranged from 70 to 127 dB re 1 μPa (median = 91 dB). SPL increased as wind speed increased, but decreased as both ice concentration and air temperature increased, and SPL increased as the number of ships per day increased. This study provides a baseline for underwater sound levels in the Canadian Arctic and fills many geographic gaps on published underwater sound levels.

Numerical-modeling-based investigation of sound transmission and reception in the short-finned pilot whale (Globicephala macrorhynchus)

The sound-transmission, beam-formation, and sound-reception processes of a short-finned pilot whale (Globicephala macrorhynchus) were investigated using computed tomography (CT) scanning and numerical simulation. The results showed that sound propagations in the forehead were modulated by the upper jaw, air components, and soft tissues, which attributed to the beam formation in the external acoustic field. These structures owned different acoustic impedance and formed a multiphasic sound transmission system that can modulate sounds into a beam. The reception pathways composed of the solid mandible and acoustic fats in the lower head conducted sounds into the tympano-periotic complex. In the simulations, sounds were emitted in the forehead transmission system and propagated into water to interrogate a steel cylinder. The resulting echoes can be interpreted from multiple perspectives, including amplitude, waveform, and spectrum, to obtain the acoustic cues of the steel cylinder. By taking the short-finned pilot whale as an example, this study provides meaningful information to further deepen our understanding of biosonar system operations, and may expand sound-reception theory in odontocetes.

Sound speed gradients in mud

Various methods have been used to estimate sound speed profiles in mud at the New England Mud Patch. Some of these methods show large sound speed gradients of order 10 s^-1. New measurements of the seabed reflection coefficient exhibit an angle of intromission over three octaves in frequency; these data constrain the range of possible sound speed gradient values. The data indicate that sound speed gradients must be quite weak, i.e., much smaller than |10 s^-1|. This conclusion is supported by core data which indicate nearly constant porosity in most of the mud layer.

Fuzzy clustering as a tool to differentiate between discrete and graded call types

Animals may communicate potential information to conspecifics using stereotyped "discrete" calls and variable "graded" calls. However, animal vocal research often centers on identifying the number of call types in a repertoire rather than quantifying the amount of gradation. Here, fuzzy clustering was applied to the social call repertoire of a species with a complex communication system, the humpback whale (Megaptera novaeangliae). Of 26 call types, 6 were classified as discrete, 7 as graded, and 13 as intermediate. These results indicate that humpback whales have a graded call repertoire, and fuzzy clustering may be a useful approach to investigate this variability.

Whistles of the pantropical spotted dolphin (Stenella attenuata) in Santos Basin, western South Atlantic Ocean

The vocal repertoire of the pantropical spotted dolphin (Stenella attenuata) is poorly documented, with no published information about acoustic signals from South Atlantic Ocean populations. We conducted passive acoustic monitoring and recording of S. attenuata population in the Santos Basin, Brazil, using a towed hydrophone array during line-transects surveys. Our monitoring yielded whistle samples derived from eight groups of S. attenuata, from which we selected 155 whistles for further analysis. Approximately 48% of the whistles presented ultrasonic frequency values, with maximum frequencies up to 31.1 kHz. Across the sample, the number of steps ranged from 0 to 20 and inflection points ranged from 0 to 8. On average, end frequencies were higher than start frequencies, and whistles generally presented wide frequency ranges, with an average of 11.3 kHz. The most predominant whistle contour category was "ascending-descending." Our study provides new information regarding the acoustic repertoire of this poorly documented species and will aid efforts for using acoustics to identify and monitor cetaceans in this region.

Identity Recognition Based on Bioacoustics of Human Body

Current biometrics rely on images obtained from the structural information of physiological characteristics, which is inherently a fatal problem of being vulnerable to spoofing. Here, we studied personal identification using the frequency-domain information based on human body vibration. We developed a bioacoustic frequency spectroscopy system and applied it to the fingers to obtain information on the anatomy, biomechanics, and biomaterial properties of the tissues. As a result, modulated microvibrations propagated through our body could capture a unique spectral trait of a person and the biomechanical transfer characteristics persisted for two months and resulted in 97.16% accuracy of identity authentication in 41 subjects. Ultimately, our method not only eliminates the practical means of creating fake copies of the relevant characteristics but also provides reliable features.

Modeling potential masking of echolocating sperm whales exposed to continuous 1-2 kHz naval sonar

Modern active sonar systems can (almost) continuously transmit and receive sound, which can lead to more masking of important sounds for marine mammals than conventional pulsed sonar systems transmitting at a much lower duty cycle. This study investigated the potential of 1-2 kHz active sonar to mask echolocation-based foraging of sperm whales by modeling their echolocation detection process. Continuous masking for an echolocating sperm whale facing a sonar was predicted for sonar sound pressure levels of 160 dB re 1 μPa², with intermittent masking at levels of 120 dB re 1 μPa², but model predictions strongly depended on the animal orientation, harmonic content of the sonar, click source level, and target strength of the prey. The masking model predicted lower masking potential of buzz clicks compared to regular clicks, even though the energy source level is much lower. For buzz clicks, the lower source level is compensated for by the reduced two-way propagation loss to nearby prey during buzzes. These results help to predict what types of behavioral changes could indicate masking in the wild. Several key knowledge gaps related to masking potential of sonar in echolocating odontocetes were identified that require further investigation to assess the significance of masking.

Echolocation click parameters of short-finned pilot whales (Globicephala macrorhynchus) in the wild

Short-finned pilot whales (Globicephala macrorhynchus) are large, deep-diving predators with diverse foraging strategies, but little is known about their echolocation. To quantify the source properties of short-finned pilot whale clicks, we made 15 deployments off the coast of Tenerife of a deep-water hydrophone array consisting of seven autonomous time-synced hydrophone recorders (SoundTraps), enabling acoustic localization and quantification of click source parameters. Of 8185 recorded pilot whale clicks, 47 were classified as being recorded on-axis, with a mean peak-to-peak source level (SL) of 181 ± 7 dB re 1 μPa, a centroid frequency of 40 ± 4 kHz, and a duration of 57 ± 23 μs. A fit to a piston model yielded an estimated half-power (-3 dB) beam width of 13.7° [95% confidence interval (CI) 13.2°-14.5°] and a mean directivity index (DI) of 22.6 dB (95% CI 22.5-22.9 dB). These measured SLs and DIs are surprisingly low for a deep-diving toothed whale, suggesting we sampled the short-finned pilot whales in a context with little need for operating a long-range biosonar. The substantial spectral overlap with beaked whale clicks emitted in similar deep-water habitats implies that pilot whale clicks may constitute a common source of false detections in beaked whale passive acoustic monitoring efforts.

Vocalization with semi-occluded airways is favorable for optimizing sound production

Vocalization in mammals, birds, reptiles, and amphibians occurs with airways that have wide openings to free-space for efficient sound radiation, but sound is also produced with occluded or semi-occluded airways that have small openings to free-space. It is hypothesized that pressures produced inside the airway with semi-occluded vocalizations have an overall widening effect on the airway. This overall widening then provides more opportunity to produce wide-narrow contrasts along the airway for variation in sound quality and loudness. For human vocalization described here, special emphasis is placed on the epilaryngeal airway, which can be adjusted for optimal aerodynamic power transfer and for optimal acoustic source-airway interaction. The methodology is three-fold, (1) geometric measurement of airway dimensions from CT scans, (2) aerodynamic and acoustic impedance calculation of the airways, and (3) simulation of acoustic signals with a self-oscillating computational model of the sound source and wave propagation.

Characterization and comparison of echolocation clicks of white-beaked dolphins (Lagenorhynchus albirostris) off the Northumberland coast, UK

Odontocetes produce ultrasonic clicks for navigation and foraging. These are commonly categorized as regular or buzz clicks based on the inter-click interval. Buzz clicks are linked to foraging behaviors and may be subdivided into slow buzz clicks for prey chase, and regular buzz clicks for prey capture. This study recorded these three click types produced by white-beaked dolphins (Lagenorhynchus albirostris) off the Northumberland coast, UK. Acoustic parameters (including duration, centroid frequency, and root-mean-squared bandwidth) were calculated and compared across the three click types. The results showed that the regular clicks had shorter durations and higher frequencies than both the buzz click types. The regular buzz clicks had longer durations, lower frequencies, and narrower bandwidths than the slow buzz clicks. Additionally, regardless of click type, about 30% of the clicks had high-frequency (200-250 kHz) secondary peaks and >90% of the clicks displayed spectral peak and notch patterns between 20 and 80 kHz. These findings are useful for future quantitative assessment of the echolocation performance of white-beaked dolphins in the wild. The patterns of spectral peaks and notches identified may facilitate for acoustic identification of this species.

Acoustic behaviour of bottlenose dolphins under human care while performing synchronous aerial jumps

Synchronous behaviours occur when two or more animals display the same behaviour at the same time. However, the mechanisms underlying this synchrony are not well understood. In this study, we carried out an experiment to determine whether or not Bottlenose dolphins use acoustic cues when performing a known synchronised exercise. For this, we recorded three dolphins while they performed requested aerial jumps both individually or synchronously in pairs, with a hydrophone array and a 360° underwater video camera allowing the identification of the subject emitting vocalisations. Results indicated that in pairs, dolphins synchronised their jumps 100% of the time. Whether they jumped alone or in pairs, they produced click trains before and after 92% of jumps. No whistles or burst-pulsed sounds were emitted by the animals during the exercise. The acoustic localisation process allowed the successful identification of the vocalising subject in 19.8% of all cases (N = 141). Our study showed that in all (n = 28) but one successful localisations, the click trains were produced by the same individual. It is worth noting that this individual was the oldest female of the group. This paper provides evidence suggesting that during synchronous behaviours, dolphins use acoustic cues, and more particularly click trains, to coordinate their movements; possibly by eavesdropping on the clicks or echoes produced by one individual leading the navigation.

A method of forensic authentication of audio recordings generated using the Voice Memos application in the iPhone

Considering the widespread use of mobile phones, audio recordings of crime scenes are widely used as digital evidence; however, it is important to authenticate the audio recordings before consideration as legal evidence. This study aimed to develop a method to authenticate audio recordings generated using the iPhone through three steps: 1) bitrate/audio latency time analysis of audio recordings, 2) comparison of the file structure/timestamp on audio recordings, and 3) device-based log history examinations for the provenance of audio recordings. Herein, we analyzed audio recording samples from ten different models of mobile handsets of the iPhone with Advanced Audio Coding (AAC) or Apple Lossless Audio Codec (ALAC), through the Voice Memos application depending on the iPhone Operating System (iOS). To analyze the characteristics of these audio recordings, we compared features including audio latency, file format/structure, and timestamps between the audio recordings generated in the iPhone and those edited through the built-in audio editing function. Furthermore, we investigated the log history registered in devices during the generation of the audio recordings. Differences in the audio latency, file size, timestamps, bitrate, and log history were confirmed on the iPhone when manipulating the audio recordings. The present results show that it is possible to verify the authentication of audio recordings generated using the Voice Memos application on iPhone.

Methods for Adventitious Respiratory Sound Analyzing Applications Based on Smartphones: A Survey

Detection and classification of adventitious acoustic lung sounds plays an important role in diagnosing, monitoring, controlling and, caring the patients with lung diseases. Such systems can be presented as different platforms like medical devices, standalone software or smartphone application. Ubiquity of smartphones and widespread use of the corresponding applications make such a device an attractive platform for hosting the detection and classification systems for adventitious lung sounds. In this paper, the smartphone-based systems for automatic detection and classification of the adventitious lung sounds are surveyed. Such adventitious sounds include cough, wheeze, crackle and, snore. Relevant sounds related to abnormal respiratory activities are considered as well. The methods are shortly described and the analyzing algorithms are explained. The analysis includes detection and/or classification of the sound events. A summary of the main surveyed methods together with the classification parameters and used features for the sake of comparison is given. Existing challenges, open issues and future trends will be discussed as well.

Automated extraction of dolphin whistles-A sequential Monte Carlo probability hypothesis density approach

The need for automated methods to detect and extract marine mammal vocalizations from acoustic data has increased in the last few decades due to the increased availability of long-term recording systems. Automated dolphin whistle extraction represents a challenging problem due to the time-varying number of overlapping whistles present in, potentially, noisy recordings. Typical methods utilize image processing techniques or single target tracking, but often result in fragmentation of whistle contours and/or partial whistle detection. This study casts the problem into a more general statistical multi-target tracking framework and uses the probability hypothesis density filter as a practical approximation to the optimal Bayesian multi-target filter. In particular, a particle version, referred to as a sequential Monte Carlo probability hypothesis density (SMC-PHD) filter, is adapted for frequency tracking and specific models are developed for this application. Based on these models, two versions of the SMC-PHD filter are proposed and the performance of these versions is investigated on an extensive real-world dataset of dolphin acoustic recordings. The proposed filters are shown to be efficient tools for automated extraction of whistles, suitable for real-time implementation.

Underwater noise characterization of down-the-hole pile driving activities off Biorka Island, Alaska

Although down-the-hole (DTH) pile driving is increasingly used for in-water pile installation, the characteristics of underwater noise from DTH pile driving is largely undocumented and unstudied. This study presents a comprehensive analysis of the noise characteristics during DTH pile driving of two steel pipe piles in shallow waters off southeast Alaska. The results showed that single-strike sound exposure levels measured at 10 m were 147 and 145 dB re 1 μPa²s with a total of 21,742 and 38,631 hammer strikes, with cumulative sound exposure levels to install each pile at 192 and 191 dB re 1 μPa²s, respectively. Though noise levels from a single strike was lower than impact pile driving of a similar pile, the cumulative sound exposure levels are likely comparable due to the much higher striking rate.

Whistles of Atlantic spotted dolphin from a coastal area in the southwestern Atlantic Ocean

Atlantic spotted dolphins were recorded on the coastal area of Rio de Janeiro with equipment of 192 kHz sampling rate. The animals produced an average of 33 whistles/min. The repertoire was balanced among four contour categories, with the occurrence of a stereotyped whistle. Frequency parameters were measured between 1.3 and 29 kHz, which represents an increase in the frequency range previously reported for this species in the southwestern Atlantic Ocean. With the use of a higher sampling rate, the acoustic parameters of S. frontalis whistles have changed significantly and became more similar to those reported for North Atlantic populations.

Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires

Animals produce vocalizations that range in complexity from a single repeated call to hundreds of unique vocal elements patterned in sequences unfolding over hours. Characterizing complex vocalizations can require considerable effort and a deep intuition about each species' vocal behavior. Even with a great deal of experience, human characterizations of animal communication can be affected by human perceptual biases. We present a set of computational methods for projecting animal vocalizations into low dimensional latent representational spaces that are directly learned from the spectrograms of vocal signals. We apply these methods to diverse datasets from over 20 species, including humans, bats, songbirds, mice, cetaceans, and nonhuman primates. Latent projections uncover complex features of data in visually intuitive and quantifiable ways, enabling high-powered comparative analyses of vocal acoustics. We introduce methods for analyzing vocalizations as both discrete sequences and as continuous latent variables. Each method can be used to disentangle complex spectro-temporal structure and observe long-timescale organization in communication.

Lima soundscape before confinement and during curfew. Airplane flights suppressions because of Peruvian lockdown

Peru declared a state of emergency on March 16 in order to prevent SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) transmissions; thus, the International Airport was closed and the soundscape of urban zones under the flight tracks have been changed in view of the fact that airplane traffic was suspended. The authors have been conducting noise monitoring since February and because of that sufficient noise data for knowing the soundscape before and during the lockdown were obtained. This article presents a case of aircraft annoyance noise in one of Lima's city districts, which is near the aircraft climbing curve, toward the ocean on departure from Lima.

Length measurement accuracy of adaptive resolution imaging sonar and a predictive model to assess adult Atlantic salmon (Salmo salar) into two size categories with long-range data in a river

Imaging sonars are used around the world for fish population monitoring. The accuracy of the length measurements has been reported in multiple studies for relatively short (<15 m) ranges and high image resolution. However, imaging sonars are often used at longer ranges (i.e., >15 m) where the images produced from sonar returns become less detailed. The accuracy of the length measurements from the Adaptive Resolution Imaging Sonar (ARIS) was tested by releasing n = 69 known-sized adult Atlantic salmon (Salmo salar) directly into the sonar field at ranges between 15 and 29 m, and measuring their echoes manually by four users and semi-automatically using a computer workflow in Echoview software. Overall, the length measurements were very variable: compared to true (fork) lengths, the mean of differences varied between -9.9 cm and 7.8 cm in the human-generated datasets, and between -42.8 cm and -20 cm in the computer-generated dataset. In addition, the length measurements in different datasets were only in poor or moderate agreement with each other (intraclass correlation <0.61). Contrary to our expectations, the distance from the transducer or the subjectively assessed echo quality did not have an effect on the measurement accuracy in most of the datasets and when it did, the effect was not systematic between the datasets. Therefore, a size class and length prediction model was implemented in a Bayesian framework to group salmon into two size categories: One-Sea-Winter (<63 cm) and Multi-Sea-Winter (≥63 cm) groups. The model correctly predicted the size category in 83% of the fish in the computer-generated dataset and ranged from 68% to 74% in the human-generated datasets. We conclude that fish length measurements derived from long-range imaging sonar data should be used with caution, but post-processing can improve the usefulness of the data for specific purposes, such as adult Atlantic salmon population monitoring.

Stroking stimulation of the skin elicits 50-kHz ultrasonic vocalizations in young adult rats

The present study aimed to clarify if stroking stimulation of the skin produces positive emotion in rats. 50-kHz ultrasonic vocalizations (USVs) were recorded as an index of the positive emotion. Stroking stimulation was applied to the ventral, dorsal, or head region of the body while the rat was in a vertical holding condition. Rats emit abundant 50-kHz USVs in response to stroking, and the number of the USVs was not different among these three stimulated regions. Other stimulations, such as light touching of the abdominal area, swinging of the body back and forth, or stroking of the external genitalia under vertical holding condition, produced significantly less 50-kHz USVs. Furthermore, different call subtypes were observed during and after stroking of the ventral region. In particular, "Trill" calls, a representative index of positive emotion, were dominant after stimulation. These results suggest that stroking of the skin induces positive emotional states.

Identification of key discriminating variables between spinner dolphin (Stenella longirostris) whistle types

Descriptions of the six different spinner dolphin (Stenella longirostris) whistle types were developed from a random sample of 600 whistles collected across a 2-yr period from a Fijian spinner dolphin population. An exploratory multivariate visualization suggested an inverse relationship between delta and minimum frequency (58.6%) as well as whistle duration (18.1%) as the most discriminating variables in this dataset. All three of these variables were deemed to be significant when considered jointly in a multivariate analysis of variance (MANOVA): delta frequency (F₅₅₉₄ = 27.167, p < 0.0001), minimum frequency (F₅₅₉₄ = 14.889, p < 0.0001), and duration (F₅₅₉₄ = 24.303, p < 0.0001). Significant differences between at least two of the whistle types were found for all five acoustic parameters in univariate analysis of variation (ANOVA) tests. Constant and sine whistles were found to be the most distinctive whistles, whereas upsweep and downsweep whistles were the most similar. The identification of which parameters differ most markedly between whistle types and the relatively high explanatory power of this study's results provide a logical starting point for objective classification of spinner dolphin whistle types using machine learning techniques.

Asthmatic versus healthy child classification based on cough and vocalised /ɑ:/ sounds

Cough is a common symptom presenting in asthmatic children. In this investigation, an audio-based classification model is presented that can differentiate between healthy and asthmatic children, based on the combination of cough and vocalised /ɑ:/ sounds. A Gaussian mixture model using mel-frequency cepstral coefficients and constant-Q cepstral coefficients was trained. When comparing the predicted labels with the clinician's diagnosis, this cough sound model reaches an overall accuracy of 95.3%. The vocalised /ɑ:/ model reaches an accuracy of 72.2%, which is still significant because the dataset contains only 333 /ɑ:/ sounds versus 2029 cough sounds.