Bioacoustic monitoring of animal vocal behavior for conservation

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.

Voice Analysis in Dogs with Deep Learning: Development of a Fully Automatic Voice Analysis System for Bioacoustics Studies

Extracting behavioral information from animal sounds has long been a focus of research in bioacoustics, as sound-derived data are crucial for understanding animal behavior and environmental interactions. Traditional methods, which involve manual review of extensive recordings, pose significant challenges. This study proposes an automated system for detecting and classifying animal vocalizations, enhancing efficiency in behavior analysis. The system uses a preprocessing step to segment relevant sound regions from audio recordings, followed by feature extraction using Short-Time Fourier Transform (STFT), Mel-frequency cepstral coefficients (MFCCs), and linear-frequency cepstral coefficients (LFCCs). These features are input into convolutional neural network (CNN) classifiers to evaluate performance. Experimental results demonstrate the effectiveness of different CNN models and feature extraction methods, with AlexNet, DenseNet, EfficientNet, ResNet50, and ResNet152 being evaluated. The system achieves high accuracy in classifying vocal behaviors, such as barking and howling in dogs, providing a robust tool for behavioral analysis. The study highlights the importance of automated systems in bioacoustics research and suggests future improvements using deep learning-based methods for enhanced classification performance.

BioSense: An automated sensing node for organismal and environmental biology

Automated remote sensing has revolutionized the fields of wildlife ecology and environmental science. Yet, a cost-effective and flexible approach for large scale monitoring has not been fully developed, resulting in a limited collection of high-resolution data. Here, we describe BioSense, a low-cost and fully programmable automated sensing platform for applications in bioacoustics and environmental studies. Our design offers customization and flexibility to address a broad array of research goals and field conditions. Each BioSense is programmed through an integrated Raspberry Pi computer board and designed to collect and analyze avian vocalizations while simultaneously collecting temperature, humidity, and soil moisture data. We illustrate the different steps involved in manufacturing this sensor including hardware and software design and present the results of our laboratory and field testing in southwestern United States.

Effective Survey Methods for the Elusive Data Deficient Black Flying Squirrel (Aeromys tephromelas) in Sabah, Malaysia Facilitate First Vocalisation Record

Flying squirrels are nocturnal, gliding relatives of tree and ground squirrels (order Sciuridae). Despite 49 species existing, literature on Asiatic flying squirrels is scarce, thus they are overlooked in conservation action plans. Recently, three species of giant flying squirrel (Aeromys tephromelas, Petaurista petaurista and Aeromys thomasi) were observed during a nocturnal mammal survey at the Rainforest Discovery Centre (RDC), an Eco centre at the edge of the Kabili-Sepilok forest reserve in Sepilok, Sabah (Malaysia, Borneo). The survey (February-March 2023) incorporated the use of red LED spotlighting, thermal imaging and bioacoustic recording during systematic along-line point counts. This is the first report on flying squirrel ecology in Sabah and the first focused publication on the 'giant' black flying squirrel (A. tephromelas), categorised by the IUCN as Data Deficient. The most notable result was the first documentation of a black flying squirrel vocalisation event (106 calls at a frequency range of 0.75-2.69 kHz and mean duration of 1.4 s). Although call function was not determined, this result sheds light on a previously unknown part of their ecology. These results stress the urgency for further research on the black flying squirrel to evaluate their current extinction risk, considering deforestation is prevalent across most of their distribution.

An exploration of ecoacoustics and its applications in conservation ecology

Our planet is facing unprecedented adversity due to the global impacts of climate change and an emerging sixth mass extinction. These impacts are exacerbated by population and industrial growth, where increased resource extraction is required to meet our insatiable demands. Yet, the tangible elements of our lone inhabited planet in the solar system are not the only things disappearing or being modified. The sounds of Earth are being altered in ways that may never be recovered. Indeed, we occupy a noisier world in this age of machines that comes at a great expense in the form of sonic extinctions. It is profoundly apparent, yet not widely recognized, that conservation efforts must consider the importance of the sonic environment (i.e., sonosphere). Although sound has been integral to life for millions of years, our understanding of its ecological role has only just begun. Sounds are one of the most important extensions of the organismic inner world, becoming testimonials of environmental complexity, integration, and relationships between apparently separated parts. From a semiotic perspective, sounds are signals utilized by many organisms to save energy in patrolling, defending, exploring, and navigating their surroundings. Sounds are tools that establish dynamic biological and ecological competencies through refined partitioning in the natural selection process of evolution. Ecoacoustics is a recent scientific discipline that aims to investigate the role of sound in ecological processes. Despite its youth, Ecoacoustics has had rapid theoretical and applied growth, consolidating a diverse array of research on the ecology of sounds across many disciplines. Here, we present how Ecoacoustics plays a significant role in conservation ecology by exploring the discipline's theoretical framework, new descriptors of sonic complexity, and innovative methods for supporting conservation efforts from singular species to entire landscapes across local and global scales. The combination of automated recording units and ecoacoustic indices present a very promising approach to the study of remote areas, rare species, and data rich analyses. While Ecoacoustics scientists continue to explore this new scientific horizon, we encourage others to consider Ecoacoustics in their conservation agendas because of its application to the study and management of terrestrial, marine, and freshwater habitats.

Extending Species-Area Relationships Into the Realm of Ecoacoustics: The Soundscape-Area Relationship

The rise in species richness with area is one of the few ironclad ecological relationships. Yet, little is known about the spatial scaling of alternative dimensions of diversity. Here, we provide empirical evidence for a relationship between the richness of acoustic traits emanating from a landscape, or soundscape richness, and island area, which we term the SoundScape-Area Relationship (SSAR). We show a positive relationship between the gamma soundscape richness and island area. This relationship breaks down at the smallest spatial scales, indicating a small-island effect. Moreover, we demonstrate a positive spatial scaling of the plot-scale alpha soundscape richness, but not the beta soundscape turnover, suggesting a direct effect of species on acoustic trait diversity. We conclude that the general scaling of biodiversity can be extended into the realm of ecoacoustics, implying soundscape metrics are sensitive to fundamental ecological patterns and useful in disentangling their complex mechanistic drivers.

Semiautomated generation of species-specific training data from large, unlabeled acoustic datasets for deep supervised birdsong isolation

Background

Bioacoustic monitoring is an effective and minimally invasive method to study wildlife ecology. However, even the state-of-the-art techniques for analyzing birdsongs decrease in accuracy in the presence of extraneous signals such as anthropogenic noise and vocalizations of non-target species. Deep supervised source separation (DSSS) algorithms have been shown to effectively separate mixtures of animal vocalizations. However, in practice, recording sites also have site-specific variations and unique background audio that need to be removed, warranting the need for site-specific data.

Methods

Here, we test the potential of training DSSS models on site-specific bird vocalizations and background audio. We used a semiautomated workflow using deep supervised classification and statistical cleaning to label and generate a site-specific source separation dataset by mixing birdsongs and background audio segments. Then, we trained a deep supervised source separation (DSSS) model with this generated dataset. Because most data is passively-recorded and consequently noisy, the true isolated birdsongs are unavailable which makes evaluation challenging. Therefore, in addition to using traditional source separation (SS) metrics, we also show the effectiveness of our site-specific approach using metrics commonly used in ornithological analyses such as automated feature labeling and species-specific trilateration accuracy.

Results

Our approach of training on site-specific data boosts the source-to-distortion, source-to-interference, and source-to-artifact ratios (SDR, SIR, and SAR) by 9.33 dB, 24.07 dB, and 3.60 dB respectively. We also find our approach allows for automated feature labeling with single-digit mean absolute percent error and birdsong trilateration accuracy with a mean simulated trilateration error of 2.58 m.

Conclusion

Overall, we show that site-specific DSSS is a promising upstream solution for wildlife audio analysis tools that break down in the presence of background noise. By training on site-specific data, our method is robust to unique, site-specific interference that caused previous methods to fail.

Complex and highly saturated soundscapes in restored oak woodlands reflect avian richness and abundance

Temperate woodlands are biodiverse natural communities threatened by land use change and fire suppression. Excluding historic disturbance regimes of periodic groundfires from woodlands causes degradation, resulting from changes in the plant community and subsequent biodiversity loss. Restoration, through prescribed fire and tree thinning, can reverse biodiversity losses, however, because the diversity of woodland species spans many taxa, efficiently quantifying biodiversity can be challenging. We assessed whether soundscapes in an eastern North American woodland reflect biodiversity changes during restoration measured in a concurrent multitrophic field study. In five restored and five degraded woodland sites in Wisconsin, USA, we sampled vegetation, measured arthropod biomass, conducted bird surveys, and recorded soundscapes for five days of every 15-day period from May to August 2022. We calculated two complementary acoustic indices: Soundscape Saturation, which focuses on all acoustically active species, and Acoustic Complexity Index (ACI), which was developed to study vocalizing birds. We used generalized additive models to predict both indices based on Julian date, time of day, and level of habitat degradation. We found that restored woodlands had higher arthropod biomass, and higher richness and abundance of breeding birds. Additionally, soundscapes in restored sites had higher mean Soundscape Saturation and higher mean ACI. Restored woodland acoustic indices exhibited greater magnitudes of daily and seasonal peaks. We conclude that woodland restoration results in higher soundscape saturation and complexity, due to greater richness and abundance of vocalizing animals. This bioacoustic signature of restoration offers a promising monitoring tool for efficiently documenting differences in woodland biodiversity.

Bioacoustic monitoring to determine addiction levels of primates to the human sphere: A feasibility study on Japanese macaques

Some nonhuman primate species, whose original habitats have been reclaimed by artificial activities, have acquired boldness toward humans which is evident based on the diminished frequency of escape behaviors. Eventually, such species have become regular users of human settlements, and are referred to as "urban primates." Considering this, we developed a noninvasive technique based on bioacoustics to provide a transparent assessment of troop addiction levels in anthropogenic environments, which are determined by the dependence on agricultural crops and human living sphere for their diets and daily ranging, respectively. We attempted to quantify the addiction levels based on the boldness of troops when raiding settlements, characterized by a "landscape of fear" because of the presence of humans as predators. We hypothesized that the boldness of troops could be measured using two indices: the frequency of raiding events on settlements and the amount of time spent there. For hypothesis testing, we devised an efficient method to measure these two indices using sound cues (i.e., spontaneous calls) for tracing troop movements that are obtainable throughout the day from most primate species (e.g., contact calls). We conducted a feasibility study of this assessment procedure, targeting troops of Japanese macaques (Macaca fuscata). For this study, we collected 346 recording weeks of data using autonomous recorders from 24 troops with different addiction levels during the nonsnowy seasons. The results demonstrated that troops that reached the threshold level, at which radical interventions including mass culling of troop members is officially permitted, could be readily identified based on the following behavioral characteristics: troop members raiding settlements two or three times per week and mean time spent in settlements per raiding event exceeding 0.4 h. Thus, bioacoustic monitoring could become a valid option to ensure the objectivity of policy judgment in urban primate management.

Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests

Tropical forest recovery is fundamental to addressing the intertwined climate and biodiversity loss crises. While regenerating trees sequester carbon relatively quickly, the pace of biodiversity recovery remains contentious. Here, we use bioacoustics and metabarcoding to measure forest recovery post-agriculture in a global biodiversity hotspot in Ecuador. We show that the community composition, and not species richness, of vocalizing vertebrates identified by experts reflects the restoration gradient. Two automated measures - an acoustic index model and a bird community composition derived from an independently developed Convolutional Neural Network - correlated well with restoration (adj-R² = 0.62 and 0.69, respectively). Importantly, both measures reflected composition of non-vocalizing nocturnal insects identified via metabarcoding. We show that such automated monitoring tools, based on new technologies, can effectively monitor the success of forest recovery, using robust and reproducible data.

Evaluating and optimising performance of multi-species call recognisers for ecoacoustic restoration monitoring

Monitoring the effect of ecosystem restoration can be difficult and time-consuming. Autonomous sensors, such as acoustic recorders, can aid monitoring across long time scales. This project successfully developed, tested and implemented call recognisers for eight species of frog in the Murray-Darling Basin. Recognisers for all but one species performed well and substantially better than many species recognisers reported in the literature. We achieved this through a comprehensive development phase, which carefully considered and refined the representativeness of training data, as well as the construction (amplitude cut-off) and the similarity thresholds (score cut-offs) of each call template used. Recogniser performance was high for almost all species examined. Recognisers for Crinia signifera, Limnodynastes fletcherii, Limnodynastes dumerilii, Litoria peronii and Crinia parinsignifera all performed well, with most templates having receiver operating characteristics values (the proportion of true positive and true negatives) over 0.7, and some much higher. Recognisers for L. peronii, L. fletcherii and L. dumerilii performed particularly well in the training data set, which allowed for responses to environmental watering events, a restoration activity, to be clearly observed. While slightly more involved than building recognisers using commercial packages, the workflows ensure that a high-quality recogniser can be built and the performance fine-tuned using multiple parameters. Using the same framework, recognisers can be improved on in future iterations. We believe that multi-species recognisers are a highly effective and precise way to detect the effects of ecosystem restoration.

An acoustic detection dataset of birds (Aves) in montane forests using a deep learning approach

BACKGROUND

Long-term monitoring is needed to understand the statuses and trends of wildlife communities in montane forests, such as those in Yushan National Park (YSNP), Taiwan. Integrating passive acoustic monitoring (PAM) with an automated sound identifier, a long-term biodiversity monitoring project containing six PAM stations, was launched in YSNP in January 2020 and is currently ongoing. SILIC, an automated wildlife sound identification model, was used to extract sounds and species information from the recordings collected. Animal vocal activity can reflect their breeding status, behaviour, population, movement and distribution, which may be affected by factors, such as habitat loss, climate change and human activity. This massive amount of wildlife vocalisation dataset can provide essential information for the National Park's headquarters on resource management and decision-making. It can also be valuable for those studying the effects of climate change on animal distribution and behaviour at a regional or global scale.

NEW INFORMATION

To our best knowledge, this is the first open-access dataset with species occurrence data extracted from sounds in soundscape recordings by artificial intelligence. We obtained seven bird species for the first release, with more bird species and other taxa, such as mammals and frogs, to be updated annually. Raw recordings containing over 1.7 million one-minute recordings collected between the years 2020 and 2021 were analysed and SILIC identified 6,243,820 vocalisations of seven bird species in 439,275 recordings. The automatic detection had a precision of 0.95 and the recall ranged from 0.48 to 0.80. In terms of the balance between precision and recall, we prioritised increasing precision over recall in order to minimise false positive detections. In this dataset, we summarised the count of vocalisations detected per sound class per recording which resulted in 802,670 occurrence records. Unlike data from traditional human observation methods, the number of observations in the Darwin Core "organismQuantity" column refers to the number of vocalisations detected for a specific bird species and cannot be directly linked to the number of individuals.We expect our dataset will be able to help fill the data gaps of fine-scale avian temporal activity patterns in montane forests and contribute to studies concerning the impacts of climate change on montane forest ecosystems on regional or global scales.

Performance of autonomous recorders to detect a cryptic and endangered primate species, the black lion-tamarin (Leontopithecus chrysopygus)

Information about species distribution is important for conservation but the monitoring of populations can demand a high sampling effort with traditional methods (e.g., line transects, sound playback) that are poorly efficient for cryptic primates, such as the black lion tamarin (Leontopithecus chrysopygus). Here we investigated the effectiveness of passive acoustic monitoring (PAM) as an alternative method to identify the presence of vocalizing lion tamarins in the wild. We aimed to: (1) determine the maximum distance at which autonomous recorders (Song Meter 3) and Raven Pro acoustic software can respectively detect and identify lion tamarin long calls emitted by two captive subjects (ex situ study); and (2) determine the sampling effort required to confirm the presence of the species in the wild (in situ study). In captive settings, we recorded lion tamarin long calls with one to two autonomous recorders operating at increasing distances from the animals' enclosure (8-202 m). In a 515 ha forest fragment, we deployed 12 recorders in a grid, 300 m apart from each other, within the estimated 100 ha home range of one group, and let them record for 10 consecutive days, totaling 985 h. In the ex situ study, hand-browsing of spectrograms yielded 298 long calls emitted from 8 to 194 m, and Raven's Template Detector identified 54.6% of them, also emitted from 8 to 194 m. In the in situ study, we manually counted 1115 long calls, and the Raven's Template Detector identified 44.75% of them. Furthermore, the presence of lion tamarins was confirmed within 1 day using four randomly sorted recorders, whereas 5 days on average were necessary with only one device. While specific protocols still need to be developed to determine primate population size using this technology, we concluded that PAM is a promising tool when considering long term costs and benefits.

A Low-Cost Multi-Purpose IoT Sensor for Biologging and Soundscape Activities

The rapid expansion in miniaturization, usability, energy efficiency, and affordability of Internet of Things (IoT) sensors, integrated with innovations in smart capability, is greatly increasing opportunities in ground-level monitoring of ecosystems at a specific scale using sensor grids. Surrounding sound is a powerful data source for investigating urban and non-urban ecosystem health, and researchers commonly use robust but expensive passive sensors as monitoring equipment to capture it. This paper comprehensively describes the hardware behind our low-cost, small multipurpose prototype, capable of monitoring different environments (e.g., remote locations) with onboard processing power. The device consists of a printed circuit board, microprocessor, local memory, environmental sensor, microphones, optical sensors and LoRa (Long Range) communication systems. The device was successfully used in different use cases, from monitoring mosquitoes enhanced with optical sensors to ocean activities using a hydrophone.

Introducing the Software CASE (Cluster and Analyze Sound Events) by Comparing Different Clustering Methods and Audio Transformation Techniques Using Animal Vocalizations

Simple Summary

Unsupervised clustering algorithms are widely used in ecology and conservation to classify animal vocalizations, but also offer various advantages in basic research, contributing to the understanding of acoustic communication. Nevertheless, there are still some challenges to overcome. For instance, the quality of the clustering result depends on the audio transformation technique previously used to adjust the audio data. Moreover, it is difficult to verify the reliability of the clustering result. To analyze bioacoustic data using a clustering algorithm, it is, therefore, essential to select a reasonable algorithm from the many existing algorithms and prepare the recorded vocalizations so that the resulting values characterize a vocalization as accurately as possible. Frequency-modulated vocalizations, whose frequencies change over time, pose a particular problem. In this paper, we present the software CASE, which includes various clustering methods and provides an overview of their strengths and weaknesses concerning the classification of bioacoustic data. This software uses a multidimensional feature-extraction method to achieve better clustering results, especially for frequency-modulated vocalizations.

Abstract

Unsupervised clustering algorithms are widely used in ecology and conservation to classify animal sounds, but also offer several advantages in basic bioacoustics research. Consequently, it is important to overcome the existing challenges. A common practice is extracting the acoustic features of vocalizations one-dimensionally, only extracting an average value for a given feature for the entire vocalization. With frequency-modulated vocalizations, whose acoustic features can change over time, this can lead to insufficient characterization. Whether the necessary parameters have been set correctly and the obtained clustering result reliably classifies the vocalizations subsequently often remains unclear. The presented software, CASE, is intended to overcome these challenges. Established and new unsupervised clustering methods (community detection, affinity propagation, HDBSCAN, and fuzzy clustering) are tested in combination with various classifiers (k-nearest neighbor, dynamic time-warping, and cross-correlation) using differently transformed animal vocalizations. These methods are compared with predefined clusters to determine their strengths and weaknesses. In addition, a multidimensional data transformation procedure is presented that better represents the course of multiple acoustic features. The results suggest that, especially with frequency-modulated vocalizations, clustering is more applicable with multidimensional feature extraction compared with one-dimensional feature extraction. The characterization and clustering of vocalizations in multidimensional space offer great potential for future bioacoustic studies. The software CASE includes the developed method of multidimensional feature extraction, as well as all used clustering methods. It allows quickly applying several clustering algorithms to one data set to compare their results and to verify their reliability based on their consistency. Moreover, the software CASE determines the optimal values of most of the necessary parameters automatically. To take advantage of these benefits, the software CASE is provided for free download.

Livestock grazing to maintain habitat of a critically endangered grassland bird: Is grazer species important?

Livestock grazing is an important management tool for biodiversity conservation in many native grasslands across the globe. Understanding how different grazing species interact with their environment is integral to achieving conservation goals. In the semiarid grasslands of Australia, grazing by sheep or cattle is used to manipulate vegetation structure to suit the habitat needs of a globally unique, critically endangered grassland bird, the plains-wanderer Pedionomus torquatus. However, there has been no investigation of whether sheep and cattle differ in their effects on plains-wanderer habitat and, therefore, it is unknown if these grazers are substitutable as a management tool. Using a grazing experiment in native grasslands over 3 years, we determined the effects of grazer type (sheep, cattle) on occurrence and vocal activity of plains-wanderer, vegetation structure and composition, and food availability. We also examined grazer effects on encounter rates of other grassland birds. Plains-wanderer breeding activity was inferred from vocalization rates captured by bioacoustic recorders. Spotlighting was used to measure encounter rates of other grassland birds. We found that different grazers altered the structure of the habitat. Grasslands grazed by cattle were typically more open, less variable, and lacked patches of dense vegetation relative to those grazed by sheep. Grazer type did not influence the likelihood of plains-wanderer occurrence, but it did interact with year of survey to affect breeding activity. The number of days with one or more calls significantly increased at sheep grazed sites in year-3, which coincided with enduring drought conditions. Similarly, grazer effects on encounter rate of all birds, bird species richness, and Australasian pipit Anthus novaeseelandiae were different between years. Dense vegetation specialists (such as stubble quail Coturnix pectoralis) were positively associated with grasslands grazed by sheep. As a habitat management tool, sheep or cattle grazing are useful when the goal is to support an open grassland structure for the plains-wanderer. However, their substitutability is likely to be dependent upon climate. We caution that a loss of dense vegetation in grasslands grazed by cattle during drought could limit the availability of optimal habitat for the plains-wanderer and habitat for other grassland birds.

Species detection framework using automated recording units: a case study of the Critically Endangered Jerdon's courser

With the advent of automated recording units, bioacoustic monitoring has become a popular tool for the collection of long-term data across extensive landscapes. Such methods involve two main components: hardware for audio data acquisition and software for analysis. In the acoustic monitoring of threatened species, a species-specific framework is often essential. Jerdon's courser Rhinoptilus bitorquatus is a Critically Endangered nocturnal bird endemic to a small region of the Eastern Ghats of India, last reported in 2008. Here we describe a reproducible and scalable acoustic detection framework for the species, comparing several commonly available hardware and detection methods and using existing software. We tested this protocol by collecting 24,349 h of data during 5 months. We analysed the data with two commercially available sound analysis programmes, following an analysis pipeline created for this species. Although we did not detect vocalizations of Jerdon's courser, this study provides a framework using a combination of hardware and software for future research that other conservation practitioners can implement. Vocal mimicry can aid or confound in detection and we highlight the potential role of mimicry in the detection of such threatened species. This species-specific acoustic detection framework can be scaled and tailored to monitor other species.

Comparing methodologies for classification of zebra finch distance calls

Bioacoustic analysis has been used for a variety of purposes including classifying vocalizations for biodiversity monitoring and understanding mechanisms of cognitive processes. A wide range of statistical methods, including various automated methods, have been used to successfully classify vocalizations based on species, sex, geography, and individual. A comprehensive approach focusing on identifying acoustic features putatively involved in classification is required for the prediction of features necessary for discrimination in the real world. Here, we used several classification techniques, namely discriminant function analyses (DFAs), support vector machines (SVMs), and artificial neural networks (ANNs), for sex-based classification of zebra finch (Taeniopygia guttata) distance calls using acoustic features measured from spectrograms. We found that all three methods (DFAs, SVMs, and ANNs) correctly classified the calls to respective sex-based categories with high accuracy between 92 and 96%. Frequency modulation of ascending frequency, total duration, and end frequency of the distance call were the most predictive features underlying this classification in all of our models. Our results corroborate evidence of the importance of total call duration and frequency modulation in the classification of male and female distance calls. Moreover, we provide a methodological approach for bioacoustic classification problems using multiple statistical analyses.

Identification, Analysis and Characterization of Base Units of Bird Vocal Communication: The White Spectacled Bulbul (Pycnonotus xanthopygos) as a Case Study

Animal vocal communication is a broad and multi-disciplinary field of research. Studying various aspects of communication can provide key elements for understanding animal behavior, evolution, and cognition. Given the large amount of acoustic data accumulated from automated recorders, for which manual annotation and analysis is impractical, there is a growing need to develop algorithms and automatic methods for analyzing and identifying animal sounds. In this study we developed an automatic detection and analysis system based on audio signal processing algorithms and deep learning that is capable of processing and analyzing large volumes of data without human bias. We selected the White Spectacled Bulbul (Pycnonotus xanthopygos) as our bird model because it has a complex vocal communication system with a large repertoire which is used by both sexes, year-round. It is a common, widespread passerine in Israel, which is relatively easy to locate and record in a broad range of habitats. Like many passerines, the Bulbul’s vocal communication consists of two primary hierarchies of utterances, syllables and words. To extract each of these units’ characteristics, the fundamental frequency contour was modeled using a low degree Legendre polynomial, enabling it to capture the different patterns of variation from different vocalizations, so that each pattern could be effectively expressed using very few coefficients. In addition, a mel-spectrogram was computed for each unit, and several features were extracted both in the time-domain (e.g., zero-crossing rate and energy) and frequency-domain (e.g., spectral centroid and spectral flatness). We applied both linear and non-linear dimensionality reduction algorithms on feature vectors and validated the findings that were obtained manually, namely by listening and examining the spectrograms visually. Using these algorithms, we show that the Bulbul has a complex vocabulary of more than 30 words, that there are multiple syllables that are combined in different words, and that a particular syllable can appear in several words. Using our system, researchers will be able to analyze hundreds of hours of audio recordings, to obtain objective evaluation of repertoires, and to identify different vocal units and distinguish between them, thus gaining a broad perspective on bird vocal communication.

Sounding the Call for a Global Library of Underwater Biological Sounds

Aquatic environments encompass the world’s most extensive habitats, rich with sounds produced by a diversity of animals. Passive acoustic monitoring (PAM) is an increasingly accessible remote sensing technology that uses hydrophones to listen to the underwater world and represents an unprecedented, non-invasive method to monitor underwater environments. This information can assist in the delineation of biologically important areas via detection of sound-producing species or characterization of ecosystem type and condition, inferred from the acoustic properties of the local soundscape. At a time when worldwide biodiversity is in significant decline and underwater soundscapes are being altered as a result of anthropogenic impacts, there is a need to document, quantify, and understand biotic sound sources–potentially before they disappear. A significant step toward these goals is the development of a web-based, open-access platform that provides: (1) a reference library of known and unknown biological sound sources (by integrating and expanding existing libraries around the world); (2) a data repository portal for annotated and unannotated audio recordings of single sources and of soundscapes; (3) a training platform for artificial intelligence algorithms for signal detection and classification; and (4) a citizen science-based application for public users. Although individually, these resources are often met on regional and taxa-specific scales, many are not sustained and, collectively, an enduring global database with an integrated platform has not been realized. We discuss the benefits such a program can provide, previous calls for global data-sharing and reference libraries, and the challenges that need to be overcome to bring together bio- and ecoacousticians, bioinformaticians, propagation experts, web engineers, and signal processing specialists (e.g., artificial intelligence) with the necessary support and funding to build a sustainable and scalable platform that could address the needs of all contributors and stakeholders into the future.

Key Issues for Realizing Open Ecoacoustic Monitoring in Australia

Many organizations are attempting to scale ecoacoustic monitoring for conservation but are hampered at the stages of data management and analysis. We reviewed current ecoacoustic hardware, software, and standards, and conducted workshops with 23 participants across 10 organizations in Australia to learn about their current practices, and to identify key trends and challenges in their use of ecoacoustics data. We found no existing metadata schemas that contain enough ecoacoustics terms for current practice, and no standard approaches to annotation. There was a strong need for free acoustics data storage, discoverable learning resources, and interoperability with other ecological modeling tools. In parallel, there were tensions regarding intellectual property management, and siloed approaches to studying species within organizations across different regions and between organizations doing similar work. This research contributes directly to the development of an open ecoacoustics platform to enable the sharing of data, analyses, and tools for environmental conservation.

First characterization of vocalizations and passive acoustic monitoring of the vulnerable African manatee (Trichechus senegalensis)

Even among the understudied sirenians, African manatees (Trichechus senegalensis) are a poorly understood, elusive, and vulnerable species that is difficult to detect. We used passive acoustic monitoring in the first effort to acoustically detect African manatees and provide the first characterization of their vocalizations. Within two 3-day periods at Lake Ossa, Cameroon, at least 3367 individual African manatee vocalizations were detected such that most vocalizations were detected in the middle of the night and at dusk. Call characteristics such as fundamental frequency, duration, harmonics, subharmonics, and emphasized band were characterized for 289 high-quality tonal vocalizations with a minimum signal-to-noise ratio of 4.5 dB. African manatee vocalizations have a fundamental frequency of 4.65 ± 0.700 kHz (mean ± SD), duration of 0.181 ± 0.069 s, 97% contained harmonics, 21% contained subharmonics, and 27% had an emphasized band other than the fundamental frequency. Altogether, the structure of African manatee vocalizations is similar to other manatee species. We suggest utilizing passive acoustic monitoring to fill in the gaps in understanding the distribution and biology of African manatees.

The Rapid Rise of Next-Generation Natural History

Many ecologists have lamented the demise of natural history and have attributed this decline to a misguided view that natural history is outdated and unscientific. Although there is a perception that the focus in ecology and conservation have shifted away from descriptive natural history research and training toward hypothetico-deductive research, we argue that natural history has entered a new phase that we call “next-generation natural history.” This renaissance of natural history is characterized by technological and statistical advances that aid in collecting detailed observations systematically over broad spatial and temporal extents. The technological advances that have increased exponentially in the last decade include electronic sensors such as camera-traps and acoustic recorders, aircraft- and satellite-based remote sensing, animal-borne biologgers, genetics and genomics methods, and community science programs. Advances in statistics and computation have aided in analyzing a growing quantity of observations to reveal patterns in nature. These robust next-generation natural history datasets have transformed the anecdotal perception of natural history observations into systematically collected observations that collectively constitute the foundation for hypothetico-deductive research and can be leveraged and applied to conservation and management. These advances are encouraging scientists to conduct and embrace detailed descriptions of nature that remain a critically important component of the scientific endeavor. Finally, these next-generation natural history observations are engaging scientists and non-scientists alike with new documentations of the wonders of nature. Thus, we celebrate next-generation natural history for encouraging people to experience nature directly.

Assessing acoustic competition between sibling frog species using rhythm analysis

Male frog advertisement calls are species-specific vocalizations used to attract females for breeding. However, it is possible for environmental or biological sounds to overlap these calls in both frequency and duration resulting in signal confusion, influencing female decision and/or location abilities. It is therefore important for vocal species competing for the same acoustic space to partition their calls either spatially or temporally (via call alternation or suppression). However, frog species previously isolated from each other may not have developed appropriate adaptive behaviors, resulting in acoustic competition. This study applied rhythm analysis to track changes in calling behavior, namely changes in calling frequency (as in beats per second), of the wallum sedgefrog and the eastern sedgefrog when vocalizing alone versus in the presence of each other to assess potential acoustic competition. Our main findings demonstrated that both species significantly altered their calling behavior when exposed to each other. While we expected the increased calling activity of one species to inhibit the activity of the other to avoid signal confusion, we instead found that both species greatly increased the beat frequency of their calls when calling in the presence of each other. We also found evidence of beat frequency development in the wallum sedgefrog whereby there was always a strong initial increase in call frequency in reaction to the first vocal interruption by the eastern sedgefrog. These results support the hypothesis that the eastern sedgefrog and the wallum sedgefrog are in competition for the acoustic space in habitats where they occur together. This highlights a new threat to the vulnerable wallum sedgefrog species and may serve to inform future management practices. Using rhythm analyses to track changes in acoustic behavior can help inform on important population dynamics such as health, trajectory, and response to management, and therefore be of great benefit to the conservation of vocal species.

The uses and implications of avian vocalizations for conservation planning

There is a growing recognition that animal behavior can affect wildlife conservation, but there have been few direct studies of animal behavior in conservation programs. However, a great deal of existing behavioral research can be applied in the context of conservation. Research on avian vocalizations provides an excellent example. The conspicuous nature of the vocal behavior of birds makes it a useful tool for monitoring populations and measuring biodiversity, but the importance of vocalizations in conservation goes beyond monitoring. Geographic song variants with population-specific signatures, or dialects, can affect territory formation and mate choice. Dialects are influenced by cultural evolution and natural selection and changes can accumulate even during the timescale of conservation interventions, such as translocations, reintroductions, and ex situ breeding. Information from existing research into avian vocalizations can be used to improve conservation planning and increase the success of interventions. Vocalizations can confer a number of benefits for conservation practitioners through monitoring, providing baseline data on populations and individuals. However, the influence of cultural variation on territory formation, mate choice, and gene flow should be taken into account because cultural differences could create obstacles for conservation programs that bring birds from multiple populations together and so reduce the success of interventions.

Bioacoustics in cognitive research: Applications, considerations, and recommendations

The multifaceted ability to produce, transmit, receive, and respond to acoustic signals is widespread in animals and forms the basis of the interdisciplinary science of bioacoustics. Bioacoustics research methods, including sound recording and playback experiments, are applicable in cognitive research that centers around the processing of information from the acoustic environment. We provide an overview of bioacoustics techniques in the context of cognitive studies and make the case for the importance of bioacoustics in the study of cognition by outlining some of the major cognitive processes in which acoustic signals are involved. We also describe key considerations associated with the recording of sound and its use in cognitive applications. Based on these considerations, we provide a set of recommendations for best practices in the recording and use of acoustic signals in cognitive studies. Our aim is to demonstrate that acoustic recordings and stimuli are valuable tools for cognitive researchers when used appropriately. In doing so, we hope to stimulate opportunities for innovative cognitive research that incorporates robust recording protocols. This article is categorized under: Neuroscience > Cognition Psychology > Theory and Methods Neuroscience > Behavior Neuroscience > Cognition.

Autonomous sound recording outperforms human observation for sampling birds: a systematic map and user guide

Autonomous sound recording techniques have gained considerable traction in the last decade, but the question remains whether they can replace human observation surveys to sample sonant animals. For birds in particular, survey methods have been tested extensively using point counts and sound recording surveys. Here, we review the latest evidence for this taxon within the frame of a systematic map. We compare sampling effectiveness of these two survey methods, the output variables they produce, and their practicality. When assessed against the standard of point counts, autonomous sound recording proves to be a powerful tool that samples at least as many species. This technology can monitor birds in an exhaustive, standardized, and verifiable way. Moreover, sound recorders give access to entire soundscapes from which new data types can be derived (vocal activity, acoustic indices). Variables such as abundance, density, occupancy, or species richness can be obtained to yield data sets that are comparable to and compatible with point counts. Finally, autonomous sound recorders allow investigations at high temporal and spatial resolution and coverage, which are more cost effective and cannot be achieved by human observations alone, even though small-scale studies might be more cost effective when carried out with point counts. Sound recorders can be deployed in many places, they are more scalable and reliable, making them the better choice for bird surveys in an increasingly data-driven time. We provide an overview of currently available recorders and discuss their specifications to guide future study designs.