Passive Acoustic Monitoring Applications Research Articles

Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists.

Recent years have seen a dramatic rise in the use of passive acoustic monitoring (PAM) for biological and ecological applications, and a corresponding increase in the volume of data generated. However, data sets are often becoming so sizable that analysing them manually is increasingly burdensome and unrealistic. Fortunately, we have also seen a corresponding rise in computing power and the capability of machine learning algorithms, which offer the possibility of performing some of the analysis required for PAM automatically. Nonetheless, the field of automatic detection of acoustic events is still in its infancy in biology and ecology. In this review, we examine the trends in bioacoustic PAM applications, and their implications for the burgeoning amount of data that needs to be analysed. We explore the different methods of machine learning and other tools for scanning, analysing, and extracting acoustic events automatically from large volumes of recordings. We then provide a step-by-step practical guide for using automatic detection in bioacoustics. One of the biggest challenges for the greater use of automatic detection in bioacoustics is that there is often a gulf in expertise between the biological sciences and the field of machine learning and computer science. Therefore, this review first presents an overview of the requirements for automatic detection in bioacoustics, intended to familiarise those from a computer science background with the needs of the bioacoustics community, followed by an introduction to the key elements of machine learning and artificial intelligence that a biologist needs to understand to incorporate automatic detection into their research. We then provide a practical guide to building an automatic detection pipeline for bioacoustic data, and conclude with a discussion of possible future directions in this field.

Gulf Toadfish (Opsanus beta) Boatwhistle Calls—A Prevalent Acoustic Cue with Passive Acoustic Monitoring Applications

Gulf Toadfish (Opsanus beta) Boatwhistle Calls—A Prevalent Acoustic Cue with Passive Acoustic Monitoring Applications

The potential of acoustic monitoring of aquatic insects for freshwater assessment.

Aquatic insects are a major indicator used to assess ecological condition in freshwater environments. However, current methods to collect and identify aquatic insects require advanced taxonomic expertise and rely on invasive techniques that lack spatio-temporal replication. Passive acoustic monitoring (PAM) is emerging as a non-invasive complementary sampling method allowing broad spatio-temporal and taxonomic coverage. The application of PAM in freshwater ecosystems has already proved useful, revealing unexpected acoustic diversity produced by fishes, amphibians, submerged aquatic plants, and aquatic insects. However, the identity of species producing sounds remains largely unknown. Among them, aquatic insects appear to be the major contributor to freshwater soundscapes. Here, we estimate the potential number of soniferous aquatic insects worldwide using data from the Global Biodiversity Information Facility. We found that four aquatic insect orders produce sounds totalling over 7000 species. This number is probably underestimated owing to poor knowledge of aquatic insects bioacoustics. We then assess the value of sound producing aquatic insects to evaluate ecological condition and find that they might be useful despite having similar responses in pristine and degraded environments in some cases. Both expert and automated identifications will be necessary to build international reference libraries and to conduct acoustic bioassessment in freshwaters. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Intertidal Soundscapes of Hardened and Living Shorelines: A Case Study of Habitat Enhancement

Abstract Organisms, such as fishes and invertebrates and including their larval stages, listen to underwater soundscapes to detect information about nearby habitats. Such soundscapes may be influenced by habitat degradation or enhancement, which can lead to acoustically mediated feedback loops affecting the overall ecosystem. Despite the importance of underwater sounds on ecological functioning, there have been limited studies documenting soundscapes of intertidal ecosystems and few, if any, of living shoreline soundscapes. Living shorelines would especially benefit from acoustically mediated effects for objectives like encouraging fish and invertebrate settlement. This case study used a Before‐After‐Control‐Impact design to sample soundscapes and nekton (i.e., fishes and mobile macroinvertebrates) at a living shoreline construction and a nearby hardened shoreline in Cedar Key, FL (USA). Diel soundscape patterns and acoustic attenuation at the two sites were also described a year following the living shoreline construction. In the acoustic sampling, the high frequency bands of both shorelines were dominated by invertebrate sounds that were influenced by season, site and time of day, while the low frequency band of the living shoreline was often dominated by a loud anthropogenic sound. About a year after the living shoreline installation—despite similar measured acoustic attenuation at both sites—the living shoreline featured louder sound pressure levels compared to the hardened shoreline, which may be particularly beneficial for promoting foundational species and other organism settlement. These results demonstrate that Gulf of Mexico intertidal habitats may have soundscape differences even within close proximity and that living shorelines may enhance acoustic characteristics in ways beneficial to continued shoreline development. This represents an important step in better understanding the relationships between habitat structures, nekton communities, and their associated soundscapes as well as the application of passive acoustic monitoring to improve coastal management and conservation.

FishSounds: A data-sharing website of global soniferous fish diversity

Active fish sound production is geographically and taxonomically widespread—though not homogenous—among fishes, including numerous commercially and recreationally important fisheries species. Despite the ecological importance of fish sounds, their passive acoustic monitoring (PAM) applications, and extensive endeavors to document them, the field of fish bioacoustics has been historically constrained by the lack of an easily accessible, comprehensive inventory of fish sound production. To create such an inventory while simultaneously assessing the global extent of known soniferous fish species, we extracted information from almost 1000 references from the years 1874–2021 to determine that over 900 fish species have been shown to produce active (i.e., intentional) sounds. Our information is collated on the FishSounds website at FishSounds.net along with representative recordings of fish sounds that can be easily searched through and accessed by our users. FishSounds has since launched a new initiative to develop an acoustic catalog for Canadian-specific fisheries species and explore their ecological characteristics, spatial distribution, and taxonomy. The data available on FishSounds can be similarly adapted to meet other regional management needs, facilitate the application of PAM, and aid in the discovery of novel soniferous behaviors across fishes globally.

Passive acoustic survey reveals the abundance of a low-density predator and its dependency on mature forests

ContextEven though habitat use is essential information for conservation management strategies, studying it in elusive and scarce forest species has proven challenging. Passive acoustic monitoring allows collecting accurate presence-absence data even for species that typically exhibit low detectability. It further provides tools for long-term and cost-effective biodiversity monitoring, and may also be used to infer population estimates at the landscape level.ObjectivesWe aimed to demonstrate the application of passive acoustic monitoring to the problem of detecting elusive species, especially in forests, using the Eurasian pygmy owl as a model species. We explored its habitat selection, and estimated occupancy and density at the landscape level in the managed boreal forest.MethodsWe conducted a wide-scale autonomous recording units (ARUs) survey, involving 292 sites in a single season, in a study area covering approx. 370 km2 in south-western Finland. We clustered the detections into territories of males to infer population size by also taking into account the size of home ranges derived from GPS-tags data. Since we were simultaneously monitoring the occupancy of a network of nest boxes and previously estimated the abundance of natural cavities, we could also estimate the proportion of pygmy owls nesting in natural cavities.ResultsIncreasing availability of mature forests was consistently the most critical habitat variable both for calling and nesting sites, increasing occupancy probability of the pygmy owls in a landscape dominated by managed forests. The proportion of sites showing occupancy by the pygmy owls was 20.9%, corresponding to an estimate of ca. Six territorial males/100 km2.ConclusionOur results confirmed that the pygmy owl can be considered a species of mature and old forests, and we provide the first data-based estimate of the total density of territorial males of this species. Passive acoustic monitoring proved to be an efficient method in detecting the presence of pygmy owls, and may overcome weaknesses of other methods, such as nest box surveys, in order to quantify population numbers.

Passive acoustic monitoring for estimating human-wildlife conflicts: The case of bee-eaters and apiculture

In human-wildlife conflicts, it is crucial to develop accurate protocols for the reliable verification of the causative species and its relationship with potential damage claims. One of such conflicts is that occurring between apiarists and bee-eaters. In this work, we aim to assess the utility of passive acoustic monitoring (PAM) as an efficient methodology to measure European bee-eater (Merops apiaster) predation pressure at beehives and its impact on honeybees foraging activity. Using Autonomous Recording Units (ARUs) in apiaries, coupled to automated recognition methods for bee-eater calls identification, we found a positive relationship between Vocal Activity Rate (VAR) index and number of bee-eaters hunting attempts on honeybees. We also found that VAR varied over time, showing a lower predation pressure during midday hours and higher during the post-breeding migratory period. Honeybees flying activity was negatively associated with VAR and this relationship was conditioned by the hour of the day. Our study offers a new application of PAM and acoustic derived indices for the evaluation of potential damages caused by wildlife. We focused on the interaction between honeybees and the European bee-eater, but we expect PAM might be useful also to remotely monitor impacts to human activities produced by other vocally active species.

Passive Bearing Estimation Using a 2-D Acoustic Vector Sensor Mounted on a Hybrid Autonomous Underwater Vehicle

The bearing estimation problem of low-frequency underwater acoustic sources is of paramount importance in antisubmarine warfare and passive acoustic monitoring applications. Typical solutions require manned vessels and towed arrays characterized by high costs, risks, and deployment difficulties. An alternative solution for such applications is represented by acoustic vector sensors (AVSs), which are lightweight, compact, and moderate in cost, have promising performance in terms of bearing discrimination and the potential to operate from autonomous underwater platforms. This article presents an experimental assessment of a 2-D AVS derived from a sonobuoy and mounted on a hybrid autonomous underwater vehicle (AUV) for estimating the bearing of underwater sources. The sensor model, its technical characteristics, the installation onboard the vehicle, and the implementation of the passive signal processing chain are reported. Three different algorithms for bearing estimation have been tested on real data: one based on a time-domain representation, one based on the frequency domain, and one relying on joint time–frequency representation. Experimentation and data are presented, first from very shallow waters within the Italian Navy base of CSSN, La Spezia, and then from the Tyrrhenian Sea, in the context of a multiplatform exercise led by the NATO S&TO CMRE. Using an acoustic navigation system as ground truth, these experiments allow the assessment of bearing estimation capabilities in both AUV (by using propeller and pump jets) and gliding navigation modes. While the results of all the three processing methods are at least par to those recently reported in similar experiments with different vector sensors and processing, the time–frequency-domain method can identify multiple sources without prior knowledge of the source signal waveform or bandwidth.

SILIC: A cross database framework for automatically extracting robust biodiversity information from soundscape recordings based on object detection and a tiny training dataset

1.Passive acoustic monitoring (PAM) offers many advantages comparing with other survey methods and gains an increasing use in terrestrial ecology, but the massive effort needed to extract species information from a large number of recordings limits its application. The convolutional neural network (CNN) has been demonstrated with its high performance and effectiveness in identifying sound sources automatically. However, requiring a large amount of training data still constitutes a challenge.2.Object detection is used to detect multiple objects in photos or videos and is effective at detecting small objects in a complex context, such as animal sounds in a spectrogram and shows the opportunity to build a good performance model with a small training dataset. Therefore, we developed the Sound Identification and Labeling Intelligence for Creatures (SILIC), which integrates online animal sound databases, PAM databases and an object detection-based model, for extracting information on the sounds of multiple species from complex soundscape recordings.3.We used the sounds of six owl species in Taiwan to demonstrate the effectiveness, efficiency and application potential of the SILIC framework. Using only 786 sound labels in 133 recordings, our model successfully identified the species' sounds from the recordings collected at five PAM stations, with a macro-average AUC of 0.89 and a mAP of 0.83. The model also provided the time and frequency information, such as the duration and bandwidth, of the sounds.4.To our best knowledge, this is the first time that the object detection algorithm has been used to identify sounds of multiple wildlife species. With an online sound-labeling platform embedded and a novel data preprocessing approach (i.e., rainbow mapping) applied, the SILIC shows its good performance and high efficiency in identifying wildlife sounds and extracting robust species, time and frequency information from a massive amount of soundscape recordings based on a tiny training dataset acquired from existing animal sound databases. The SILIC can help expand the application of PAM as a tool to evaluate the state of and detect the change in biodiversity by, for example, providing high temporal resolution and continuous information on species presence across a monitoring network.

Fish sounds near Sachs Harbour and Ulukhaktok in Canada’s Western Arctic

The sounds of Arctic marine fishes recorded in situ have been largely ignored in the literature, despite the successful application of passive acoustic monitoring (PAM) for mapping the presence of fishes at lower latitudes. Polar cod, Boreogadus saida, is a known soniferous species that holds keystone status in the Arctic and for which PAM could be a useful tool for understanding their distributions-particularly during their spawning seasons during the Arctic winter. PAM recordings from Sachs Harbour and Ulukhaktok (covering 1.5 years between them) were analysed for fish sounds. A total of 44 grunts and 3 knocks were recorded, the majority of which were recorded at Ulukhaktok during January. A difference in the number of fish calls was also seen between months with the highest number occurring during January. No diurnal patterns in the number of fish calls were observed. K-means cluster analyses based on the 90% bandwidth, duration, peak and centre frequencies showed three clusters. Type 1 occurred during October through April, Type 2 from October through February and Type 3 during October. Type 2 calls were only noted near Ulukhaktok and Type 1 calls resembled those from polar cod. These results either suggest different species or the vocal repertoire of a single species. Based on the spectral and temporal characteristics, all fish sounds resembled those from gadids. The detection of fish calls and the apparent spatio-temporal variation following the expected spawning season of polar cod illustrate the potential usefulness of PAM for Arctic fishes—particularly at a time when climate change is forcing Arctic nations to better prepare for ecological shifts and changing predator/prey relationships.

Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring

Abstract High‐throughput environmental sensing technologies are increasingly central to global monitoring of the ecological impacts of human activities. In particular, the recent boom in passive acoustic sensors has provided efficient, noninvasive, and taxonomically broad means to study wildlife populations and communities, and monitor their responses to environmental change. However, until recently, technological costs and constraints have largely confined research in passive acoustic monitoring (PAM) to a handful of taxonomic groups (e.g., bats, cetaceans, birds), often in relatively small‐scale, proof‐of‐concept studies. The arrival of low‐cost, open‐source sensors is now rapidly expanding access to PAM technologies, making it vital to evaluate where these tools can contribute to broader efforts in ecology and biodiversity research. Here, we synthesise and critically assess the current emerging opportunities and challenges for PAM for ecological assessment and monitoring of both species populations and communities. We show that terrestrial and marine PAM applications are advancing rapidly, facilitated by emerging sensor hardware, the application of machine learning innovations to automated wildlife call identification, and work towards developing acoustic biodiversity indicators. However, the broader scope of PAM research remains constrained by limited availability of reference sound libraries and open‐source audio processing tools, especially for the tropics, and lack of clarity around the accuracy, transferability and limitations of many analytical methods. In order to improve possibilities for PAM globally, we emphasise the need for collaborative work to develop standardised survey and analysis protocols, publicly archived sound libraries, multiyear audio datasets, and a more robust theoretical and analytical framework for monitoring vocalising animal communities.

Air movement sound production by alewife, white sucker, and four salmonid fishes suggests the phenomenon is widespread among freshwater fishes.

We sought to describe sounds of some of the common fishes suspected of producing unidentified air movement sounds in soundscape surveys of freshwater habitats in the New England region of North America. Soniferous behavior of target fishes was monitored in real time in the field in both natural and semi-natural environments by coupling Passive Acoustic Monitoring (PAM) with direct visual observation from shore and underwater video recording. Sounds produced by five species including, alewife (Alosa pseudoharengus, Clupeidae), white sucker (Catastomus commersonii, Catostomidae), brook trout (Salvelinus fontinalis, Salmonidae), brown trout (Salmo trutta, Salmonidae), and rainbow trout (Oncorhynchus mykiss, Salmonidae) were validated and described in detail for the first time. In addition, field recordings of sounds produced by an unidentified salmonid were provisionally attributed to Atlantic salmon (Salmo salar, Salmonidae). Sounds produced by all species are of the air movement type and appear to be species specific. Our data based on fishes in three distinct orders suggest the phenomenon may be more ecologically important than previously thought. Even if entirely incidental, air movement sounds appear to be uniquely identifiable to species and, hence, hold promise for PAM applications in freshwater and marine habitats.

Potential for use of passive acoustic monitoring of piranhas in the Pacaya–Samiria National Reserve in Peru

Abstract Passive acoustic monitoring (PAM) can be an effective tool in the identification of fishes and mapping of their temporal and spatial distribution patterns and thereby aids in ecosystem management in remote locations. However, to date measurements of the acoustic properties of piranha have been primarily made in aquaria on captive specimens obtained through the aquarium trade. Data on the sound production of wild piranha taken under natural field conditions would enhance PAM applications. Piranha captured as part of routine annual monitoring by Operation Wallacea within the Pacaya–Samiria National Reserve in Peru were auditioned for sound production prior to release. Auditioning was done by gently holding a fish underwater in the river near a suspended hydrophone, thus recordings included the piranha sound as well as natural ambient sound. Seventy‐nine per cent of the 129 auditioned piranha, including Pygocentrus nattereri, Serrasalmus maculatus, Serrasalmus cf. sanchezi and an unidentified Serrasalmus spp. complex, produced sounds consisting of 2 to 23 barks in a sequential series. Sound production by S. maculatus and S. cf. sanchezi are reported for the first time. Bark characteristics exhibited high variation within bark series of individual fish. Piranha could not be distinguished by single variables but did exhibit significant multivariate differences. The relationship of several variables to fish size also differed significantly among species. Measurements of within‐fish variation and bark series pattern attributes were found to be useful for discrimination of sounds among piranha species. We demonstrate that closely related species of piranha can be distinguished by their sounds under natural acoustic conditions based on multivariate analyses, suggesting that passive acoustic monitoring (PAM) can be an effective tool for ecosystem management in the Amazon. More broadly, our study also suggests (a) the need to report more detailed statistical descriptions of fish sounds, including measures of within‐fish variation, (b) the importance of describing characteristics of sound series produced by fishes in addition to those of individual sound types and (c) the need to deposit museum voucher specimens to anchor specific sounds to specific individuals.

Snapping shrimp sound production patterns on Caribbean coral reefs: relationships with celestial cycles and environmental variables

The rich acoustic environment of coral reefs, including the sounds of a variety of fish and invertebrates, is a reflection of the structural complexity and biological diversity of these habitats. Emerging interest in applying passive acoustic monitoring and soundscape analysis to measure coral reef habitat characteristics and track ecological patterns is hindered by a poor understanding of the most common and abundant sound producers on reefs—the snapping shrimp. Here, we sought to address several basic biophysical drivers of reef sound by investigating acoustic activity patterns of snapping shrimp populations on two adjacent coral reefs using a detailed snap detection analysis routine to a high-resolution 2.5-month acoustic dataset from the US Virgin Islands. The reefs exhibited strong diel and lunar periodicity in snap rates and clear spatial differences in snapping levels. Snap rates peaked at dawn and dusk and were higher overall during daytime versus nighttime, a seldom-reported pattern in earlier descriptions of diel snapping shrimp acoustic activity. Small differences between the sites in snap rate rhythms were detected and illustrate how analyses of specific soundscape elements might reveal subtle between-reef variation. Snap rates were highly correlated with environmental variables, including water temperature and light, and were found to be sensitive to changes in oceanographic forcing. This study further establishes snapping shrimp as key players in the coral reef chorus and provides evidence that their acoustic output reflects a combination of environmental conditions, celestial influences, and spatial habitat variation. Effective application of passive acoustic monitoring in coral reef habitats using snap rates or snapping-influenced acoustic metrics will require a mechanistic understanding of the underlying spatial and temporal variation in snapping shrimp sound production across multiple scales.

Characterization of the complexity in short oscillating time series: An application to seismic airgun detonations.

Extracting frequency-derived parameters allows for the identification and characterization of acoustic events, such as those obtained in passive acoustic monitoring applications. Situations where it is difficult to achieve the desired frequency resolution to distinguish between similar events occur, for example, in short time oscillating events. One feasible approach to make discrimination among such events is by measuring the complexity or the presence of non-linearities in a time series. Available techniques include the delay vector variance (DVV) and recurrence plot (RP) analysis, which have been used independently for statistical testing, however, the similarities between these two techniques have so far been overlooked. This work suggests a method that combines the DVV method with the recurrence quantification analysis parameters of the RP graphs for the characterization of short oscillating events. In order to establish the confidence intervals, a variant of the pseudo-periodic surrogate algorithm is proposed. This allows one to eliminate the fine details that may indicate the presence of non-linear dynamics, without having to add a large amount of noise, while preserving more efficiently the phase-space shape. The algorithm is verified on both synthetic and real world time series.

Counting odontocetes from click train detections using multiple independent autonomous acoustic sensors

Passive acoustic monitoring (PAM) is often suggested as an effective technology to mitigate impacts from anthropogenic activities; however, the ability to reliably and efficiently detect, locate, and count cetaceans using PAM is still in development. One particularly useful application of PAM is species density estimation, which requires an estimate of the number of individuals involved in a detection event. Efforts have been undertaken to develop methods to directly count the number of vocalizing animals during acoustic detection events. For odontocetes, discrete clicks are almost indistinguishable between individuals, making it more difficult to determine the number of vocalizing animals as the number increases. Using recordings from multiple closely spaced (≈200 m) GuardBuoy sensors deployed on the Canadian Scotian Shelf, cross-sensor correlograms were produced to estimate the number of individual sperm whales, and, as a more challenging case, the number of vocalizing delphinids. Using feature-based multipath reflection discrimination, the raw time series were reduced to a synthetic time series of binary click detections with the multipath arrivals removed. The synthesized click-detection time series were used for the cross-sensor correlograms to generate improved estimates of the number of vocalizing animals as compared with using the raw time series.

Drive level acoustic defectoscopy for head disc interface (HDI) integration and manufacturing

A short product development cycle for modern hard disc drive (HDD) depends entirely on early defect characterization. However, deviations from desired HDI mechanical performance are not always manifested in electromagnetic read back or servo control signals. The best non-destructive tool to monitor HDI dynamics at the HDD level is a passive acoustic technique that tracks equilibrium of the head gimbal assembly and air bearing (AB) modes. This technique is capable of active head protrusion detection as well as detection of embedded particles. The drawback of the passive acoustic monitoring technique at drive level is that a noisy HDD environment makes the detectability of a useful signal challenging. The proposed HDD level in situ passive HDI acoustic monitoring technique consists of external AE sensors driven by adequate electronics. These are enhanced by advanced signal processing routines that include adaptive discrete wavelet transforms. The system is tuned to monitor acoustic HDI signatures during drive spin-up/spin-down cycles as well as during passive and active height across the data zones. In addition, integrated acoustic energy metrics can be used in AB design evaluation stages. In this work examples of HDD level passive acoustic monitoring applications for HDI development, and manufacturing defect tracking, are presented and supported by failure analysis results.