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In urban green space (UGS), diverse acoustic resources are essential for enhancing the public's well-being, as different sound sources can bring distinct auditory experiences. However, there is a lack of research on the sources-based soundscapes diversity (SSD). In this study, we fill this gap by exploring the characteristics of SSD in different recreational areas of UGSs across three urban gradients in Fuzhou City, China. Additionally, we investigated the relationship between SSD, landscape elements (LEs), and landscape patterns (LPs) at various spatial scales. Our findings identified 53 types of sound sources, predominantly biological sound and anthrophony. The alpha and beta diversity indices effectively characterized SSD in UGSs and captured the differences in three levels of urbanization. Our results showed that urbanization gradients significantly impact soundscape diversity, especially in terms of animal sounds and mechanical sounds. For LEs, we found that trees play a crucial role in enhancing the diversity of animal and recreation sounds, whereas the proportion of buildings negatively correlates with animal sounds. Our analysis between LPs and SSDs revealed a strong correlation between the built environment (mainly roads) and lawn with traffic sound. In contrast, a higher percentage of forest land was associated with more animal sounds and fewer recreational and mechanical sounds. Characterizing urbanization gradients and the factors affecting SSD can be used to guide the development and enhancement of soundscapes in UGS. Highlights: The urban green space in different urban gradients and green space types have varying soundscape diversity characteristics. Urbanization mainly affects animal sounds and mechanical sounds. Trees play a crucial role in enhancing the diversity of animal and recreational sounds. Built environment (mainly roads) and lawn ratio significantly correlates with traffic sounds, while the percentage of forest land attracts more animal sounds and weakens recreational and mechanical sounds. [ABSTRACT FROM AUTHOR]

Machine learning approaches for pattern recognition are increasingly popular. However, the underlying algorithms are often not open source, may require substantial data for model training, and are not geared toward specific tasks. We used open-source software to build a green toad breeding call detection algorithm that will aid in field data analysis. We provide instructions on how to reproduce our approach for other animal sounds and research questions. Our approach using 34 green toad call sequences and 166 audio files without green toad sounds had an accuracy of 0.99 when split into training (70%) and testing (30%) datasets. The final algorithm was applied to amphibian sounds newly collected by citizen scientists. Our function used three categories: "Green toad(s) detected", "No green toad(s) detected", and "Double check". Ninety percent of files containing green toad calls were classified as "Green toad(s) detected", and the remaining 10% as "Double check". Eighty-nine percent of files not containing green toad calls were classified as "No green toad(s) detected", and the remaining 11% as "Double check". Hence, none of the files were classified in the wrong category. We conclude that it is feasible for researchers to build their own efficient pattern recognition algorithm. [ABSTRACT FROM AUTHOR]

Background: Previous research has consistently shown value in studying emotion processing with psychopathy, but the relative effect of aural and visual stimulation has hardly been considered. Aims: Our aims were to (1) compare reaction time and self‐rated valence ((un)pleasantness), arousal and sense of being dominated by affective sounds or visual images among offenders with and without psychopathy (=/>26) on the psychopathy check‐list revised; (2) investigate any associations, controlling for social desirability and depression; (3) explore the possible mediating effect of criminogenic cognitions on any relationships between psychopathy and emotional responses to affective stimuli. Methods: Professional educators invited all male offenders serving semi‐open custody sentences in one prison to participate. After a semi structured interview to assess psychopathy, they self‐reported on criminogenic cognition, depression and social desirability scales, before a computer task using standardised human and animal sounds and images. Using the self‐assessment manikin, participants rated the emotional valence, arousal and dominance levels when pleasant, unpleasant and neutral sounds or images were presented. Results: About one in three prisoners completed all the ratings—120 men, of mean age 38.8 years (SD = 11.06). All had shorter reaction times to sounds than images. Offenders with high affective‐interpersonal factor scores rated all types of sounds as less pleasant. Men with high psychopathy scores took longer to respond to unpleasant images than those with lower scores. There was a positive relationship between psychopathy factors and criminogenic cognitions and the external locus of criminogenic cognitions mediated the relationship between psychopathy facets and emotional responses of valence to pleasant sounds. Conclusion: Our findings confirm the potential importance of emotional reactions to sounds as well as images among men with psychopathy. Given the shorter reaction times to sounds, in real life sounds could prove more provocative than images. While only small differences emerged between men above and below the PCL‐R threshold, indications from facet analyses suggest that further study of sound stimuli could enhance the understanding of emotional response differences to inform interventions. Future research in this area should focus on human‐related stimuli and add alexithymia measures. [ABSTRACT FROM AUTHOR]

Behavior represents animals' primary means of responding to environmental variation and adapting to rapid environmental change. Many animals' presence, let alone behavior, is highly cryptic to human observers, presenting a significant barrier in both theoretical and applied behavioral ecology. Bioacoustic signals not only reveal animals' presence, but also encode detailed information about the behaviors in which they are engaging. The study of behavioral bioacoustics has emerged to decipher the context and function of animal sounds and to apply this comprehension to understanding animal behavior across ecological scales and levels of biological organization. Growing capacity for behavioral bioacoustics represents a profound opportunity to understand animal behavior and steward rapidly changing ecosystems in the Anthropocene. Interpreting sound gives powerful insight into the health of ecosystems. Beyond detecting the presence of wildlife, bioacoustic signals can reveal their behavior. However, behavioral bioacoustic information is underused because identifying the function and context of animals' sounds remains challenging. A growing acoustic toolbox is allowing researchers to begin decoding bioacoustic signals by linking individual and population-level sensing. Yet, studies integrating acoustic tools for behavioral insight across levels of biological organization remain scarce. We aim to catalyze the emerging field of behavioral bioacoustics by synthesizing recent successes and rising analytical, logistical, and ethical challenges. Because behavior typically represents animals' first response to environmental change, we posit that behavioral bioacoustics will provide theoretical and applied insights into animals' adaptations to global change. [ABSTRACT FROM AUTHOR]

The Homeric verbs μύκε "moo" λάκε "screech" and μακών "bleat(ing)" describe animal sounds but can also apply to inanimate subjects in certain contexts. This paper considers the morphology semantics and poetics of these verbs within two frameworks: media theory and animacy studies. Through these verbs the poet creates a complex nonhuman soundworld of objects animals bodies and material media that challenges the limits of human perception. Close analyses of the passages in which these verbs appear reveal the medial and animate complexities of well-known epic objects such as Achilles' shield Hector's body or the cloud-gates of Olympus. The sounds of these and other things are embedded but not wholly translated into human sensory understanding; the scenes in question also challenge easy distinctions between humans and nonhumans animals and objects and living and dead bodies. [ABSTRACT FROM AUTHOR]

The article "Music in Star Trek: Sound, Utopia, and the Future" examines the role of music in the Star Trek franchise and its connection to themes of utopia, technology, and religion. The authors analyze the use of music in various Star Trek series and films to convey utopian ideals and explore the impact of technology and artificial intelligence on humanity. They also discuss the inclusion of religion in Star Trek, particularly in the show Deep Space Nine, and how it is validated through music, although it sometimes exoticizes certain cultures. The article also explores the use of sound in Star Trek, including its portrayal in online fan spaces and the characterization of characters. The authors suggest that while the book provides a comprehensive exploration of music in Star Trek, it could benefit from a deeper consideration of the show's relation to utopian theories. The article acknowledges the diverse perspectives and themes present in Star Trek, such as issues of identity, equality, diversity, and human agency. Overall, the article offers valuable insights for fans, scholars, and musicians interested in the cultural significance of music in Star Trek. [Extracted from the article]

Simple Summary: This study addresses the significance of animal sounds as valuable indicators of both behavior and health in animals, emphasizing the challenges involved in collecting datasets for deep learning models. Particularly, in the context of classifying pig vocalization and non-vocalization, it is identified as laborious and time-consuming when relying on human efforts. In response to these challenges, the research proposes a new approach utilizing a deep learning model to automatically classify pig vocalization and non-vocalization with high accuracy. The success of this method not only provides an efficient means of collecting pig sound datasets but also presents a promising avenue for improving the classification of pig vocalization and non-vocalization in deep learning models, thereby contributing to advancements in animal behavior research and health monitoring. Since pig vocalization is an important indicator of monitoring pig conditions, pig vocalization detection and recognition using deep learning play a crucial role in the management and welfare of modern pig livestock farming. However, collecting pig sound data for deep learning model training takes time and effort. Acknowledging the challenges of collecting pig sound data for model training, this study introduces a deep convolutional neural network (DCNN) architecture for pig vocalization and non-vocalization classification with a real pig farm dataset. Various audio feature extraction methods were evaluated individually to compare the performance differences, including Mel-frequency cepstral coefficients (MFCC), Mel-spectrogram, Chroma, and Tonnetz. This study proposes a novel feature extraction method called Mixed-MMCT to improve the classification accuracy by integrating MFCC, Mel-spectrogram, Chroma, and Tonnetz features. These feature extraction methods were applied to extract relevant features from the pig sound dataset for input into a deep learning network. For the experiment, three datasets were collected from three actual pig farms: Nias, Gimje, and Jeongeup. Each dataset consists of 4000 WAV files (2000 pig vocalization and 2000 pig non-vocalization) with a duration of three seconds. Various audio data augmentation techniques are utilized in the training set to improve the model performance and generalization, including pitch-shifting, time-shifting, time-stretching, and background-noising. In this study, the performance of the predictive deep learning model was assessed using the k-fold cross-validation (k = 5) technique on each dataset. By conducting rigorous experiments, Mixed-MMCT showed superior accuracy on Nias, Gimje, and Jeongeup, with rates of 99.50%, 99.56%, and 99.67%, respectively. Robustness experiments were performed to prove the effectiveness of the model by using two farm datasets as a training set and a farm as a testing set. The average performance of the Mixed-MMCT in terms of accuracy, precision, recall, and F1-score reached rates of 95.67%, 96.25%, 95.68%, and 95.96%, respectively. All results demonstrate that the proposed Mixed-MMCT feature extraction method outperforms other methods regarding pig vocalization and non-vocalization classification in real pig livestock farming. [ABSTRACT FROM AUTHOR]

This paper describes an unusual underwater biological chorus recorded in the Southern California Bight and presents a numerical modeling approach that replicates aspects of the chorus. During one experiment, the evolution of the directionality of the chorusing region over time is suggestive of "The Wave", the human waves performed by fans periodically standing and sitting in sports stadia around the world; here, the region of chorusing periodically propagated upcoast over 20 km of coastline at nearly 1.5 km/s. The chorus occurs predominantly at night in spring and summer, mostly in very shallow waters near the coast. It increases the underwater sound field levels within chorusing regions by 20-30 dB in the 50 Hz to 1 kHz frequency band. The chorus is composed of three parts; 1) a "sunset chorus" which is a 20-to-30 min continuous roar around sunset with received spectral levels up to 100 dB re 1 µPa²/Hz, 2) a "sunrise chorus" of lower level than the sunset chorus, and 3) an all-night-long cycling chorus. The cycling portion is made up of 15-to-20 s periods of higher received spectral levels (up to 90 dB re 1 µPa²/Hz within the chorusing region) each followed by a 10-to-20 s lull in which the spectral levels drop by 4-10 dB. This alternating pattern repeats every 30-40 s throughout the night. The numerical modeling approach is based on the physics of excitable media. A cellular automaton is used to model the two-dimensional spatial grid occupied by the calling animals (units), with each unit being either in the "resting", "excitable", or "active (calling)" state at each time step. Transition from resting to excitable and from active back to resting occurs automatically after a fixed period of time in the present state, whereas the probability of transitioning from excitable to active is determined not only by the elapsed time since entering the excitable state, but also by the received sound level at the unit location, creating a non-linear acoustics-based coupling between units. With appropriate inputs, many determined from measurements of the chorus properties and the individual animal calls themselves, simulations with the model can replicate the cycling levels in the night-long chorus, the continuous din of the sunset chorus, and (once properly initialized) the periodic upcoast evolution of the chorusing region ("The Wave"). When noise from a transiting ship is included in the simulations, the spatiotemporal characteristics of the chorus change appreciably, in ways similar to changes observed during the experiment. [ABSTRACT FROM AUTHOR]

Sound production during feeding by the pot‐bellied seahorse, Hippocampus abdominalis, was quantified with an observation of clicks (acoustic signal) and snicks (visual behavior). Female, male, and juvenile seahorses had feeding sounds characterized for peak (dominant) frequency (Hz), sound pressure level (SPL), and duration (ms). Subject body size and condition was estimated by standard length (SL, cm), to determine an estimate of body condition index (BCI). An inverse correlation between mean peak frequency (Hz) of clicks and SL was found for females. A negative correlation between peak frequency (Hz) of clicks and a residual BCI was determined for both males and females, suggesting that acoustic signals may contain information regarding fitness. [ABSTRACT FROM AUTHOR]

The study of animal sounds in biology and ecology relies heavily upon time–frequency (TF) visualisation, most commonly using the short‐time Fourier transform (STFT) spectrogram. This method, however, has inherent bias towards either temporal or spectral details that can lead to misinterpretation of complex animal sounds. An ideal TF visualisation should accurately convey the structure of the sound in terms of both frequency and time, however, the STFT often cannot meet this requirement. We evaluate the accuracy of four TF visualisation methods (superlet transform [SLT], continuous wavelet transform [CWT] and two STFTs) using a synthetic test signal. We then apply these methods to visualise sounds of the Chagos blue whale, Asian elephant, southern cassowary, eastern whipbird, mulloway fish and the American crocodile. We show that the SLT visualises the test signal with 18.48%–28.08% less error than the other methods. A comparison between our visualisations of animal sounds and their literature descriptions indicates that the STFT's bias may have caused misinterpretations in describing pygmy blue whale songs and elephant rumbles. We suggest that use of the SLT to visualise low‐frequency animal sounds may prevent such misinterpretations. Finally, we employ the SLT to develop 'BASSA', an open‐source, GUI software application that offers a no‐code, user‐friendly tool for analysing short‐duration recordings of low‐frequency animal sounds for the Windows platform. The SLT visualises low‐frequency animal sounds with improved accuracy, in a user‐friendly format, minimising the risk of misinterpretation while requiring less technical expertise than the STFT. Using this method could propel advances in acoustics‐driven studies of animal communication, vocal production methods, phonation and species identification. [ABSTRACT FROM AUTHOR]

Machine learning tools capable of identifying animals by sound have proliferated, making the challenge of interpreting their outputs much more prevalent. These tools, like their predecessors, quantify prediction uncertainty with scores that tend to resemble probabilities but are actually unitless scores that are (generally) positively related to prediction accuracy in species-specific ways. BirdNET is one such tool, a freely available animal sound identification algorithm capable of identifying > 6,000 species, most of them birds. We describe two ways in which BirdNET "confidence scores"—and the output scores of other detector tools—can be used appropriately to interpret BirdNET results (reviewing them down to a user-defined threshold or converting them to probabilities), and provide a step-by-step tutorial to follow these suggestions. These suggestions are complementary to common performance metrics like precision, recall, and receiver operating characteristic. BirdNET can be a powerful tool for acoustic-based biodiversity research, but its utility depends on the careful use and interpretation of its outputs. [ABSTRACT FROM AUTHOR]

Many animal species depend on sound to communicate with conspecifics. However, human‐generated (anthropogenic) noise may mask acoustic signals and so disrupt behavior. Animals may use various strategies to circumvent this, including shifts in the timing of vocal activity and changes to the acoustic parameters of their calls. We tested whether pied tamarins (Saguinus bicolor) adjust their vocal behavior in response to city noise. We predicted that both the probability of occurrence and the number of long calls would increase in response to anthropogenic noise and that pied tamarins would temporally shift their vocal activity to avoid noisier periods. At a finer scale, we anticipated that the temporal parameters of tamarin calls (e.g., call duration and syllable repetition rate) would increase with noise amplitude. We collected information on the acoustic environment and the emission of long calls in nine wild pied tamarin groups in Manaus, Brazil. We found that the probability of long‐call occurrence increased with higher levels of anthropogenic noise, though the number of long calls did not. The number of long calls was related to the time of day and the distance from home range borders—a proxy for the distance to neighboring groups. Neither long‐call occurrence nor call rate was related to noise levels at different times of day. We found that pied tamarins decreased their syllable repetition rate in response to anthropogenic noise. Long calls are important for group cohesion and intergroup communication. Thus, it is possible that the tamarins emit one long call with lower syllable repetition, which might facilitate signal reception. The occurrence and quantity of pied tamarin' long calls, as well as their acoustic proprieties, seem to be governed by anthropogenic noise, time of the day, and social mechanisms such as proximity to neighboring groups. Highlights: Anthropogenic noise may mask animal sounds and to circumvent this, animals may use various behavioral strategies.Pied tamarins increase call occurrence and reduce syllable repetition rate in response to anthropogenic noise.The number of calls emitted was not related to anthropogenic noise levels, but the time of day and the distance from the border of their home ranges, proxy for the distance to neighboring groups. [ABSTRACT FROM AUTHOR]

The proliferation of small autonomous recorders makes it easier than ever to sample terrestrial acoustic animals and soundscapes. I conducted a comparison of four small recorders to evaluate their performance in a field setting: Wildlife Acoustics Song Meter Mini; Wildlife Acoustics Song Meter Micro; Open Acoustics Audiomoth; and Cornell SwiftOne. I address two questions: (1) How do in-person point counts compare to recorder-based point counts using these small autonomous recorders? (2) How does the quality of the recordings compare across these small autonomous recorders? To evaluate the performance of the recorders in point counts, I conducted in-person and recording-based point counts at ten locations. Each of the recorders performed similarly well at point counts, producing comparable estimates of species richness, although all of the autonomous recorders under-estimated species richness. To evaluate recording quality, I conducted a sound transmission test, broadcasting and rerecording sounds. Recorders varied in their frequency response above 12 kHz, but showed only subtle differences in the frequency response at frequencies below 12 kHz. I conclude that each of these types of small recorders provide bioacousticians with useful tools for conducting point counts, and for passive monitoring of animal sounds, with only subtle differences across the investigated models. [ABSTRACT FROM AUTHOR]

Ecological disturbances are becoming more extensive and intensive globally, a trend exemplified by 'megafires' and industrial deforestation, which cause widespread losses of forest cover. Yet the hypothesis that contemporary environmental disturbances are affecting biodiversity has been difficult to test directly.The novel combination of landscape‐scale passive acoustic monitoring, a new machine learning algorithm, BirdNET and improved Bayesian model‐fitting engines enables cohesive, community‐level before‐after, control‐impact studies of disturbances. We conducted such a study of a 2020 megafire in the Sierra Nevada, USA. We used a bespoke dynamic multi‐species occupancy modelling approach, which enabled us to account for imperfect detection, misclassifications, and to share information among species.There was no community‐level difference in colonization between burned and unburned forest. In contrast, the probability of site extinction in burned forest, 0.36, was significantly higher than in unburned forest, 0.12. Of the 67 species in our study, 6 (9%) displayed a positive colonization response to the fire, while 28 (41%) displayed a significant extinction response.We observed a 12% decrease in avian biodiversity 1 year post‐fire, and a substantial shift in community composition. However, in this ecosystem, many species display time‐dependent responses to the fire that are unobservable after just 1 year.Synthesis and applications. We have shown that three emerging conservation technologies, passive acoustic monitoring, machine learning animal sound identification algorithms, and advances in Bayesian statistical tools, can provide previously unattainable information about biodiversity responses to ecological change. Critically, our approach is transferrable and scalable, as the workflow is agnostic to species or ecosystem and each component is either freely available (all relevant software) or relatively inexpensive (recording hardware). Environmental change is unfolding rapidly, but new analytical techniques may help our understanding and—thus interventions—keep pace. [ABSTRACT FROM AUTHOR]

Moose are a popular species with recreationists but understudied acoustically. We used publicly available videos to characterize and quantify the vocalizations of moose in New Hampshire separated by age/sex class. We found significant differences in peak frequency, center frequency, bandwidth, and duration across the groups. Our results provide quantification of wild moose vocalizations across age/sex classes, which is a key step for passive acoustic detection of this species and highlights public videos as a potential resource for bioacoustics research of hard-to-capture and understudied species. [ABSTRACT FROM AUTHOR]

The text provides a compilation of book reviews from the journal Ibis, focusing on various topics related to ornithology. The Alexander Library at the Edward Grey Institute of Field Ornithology in Oxford, UK, houses the books reviewed and welcomes additional literature for review. One book, "The Sahel: A Great Green Wall," explores the efforts of African countries to create a Great Green Wall in the Sahel region, with a focus on bird populations and their habitats. Another book, "Field Guide to North American Flycatchers: Empidonax and Pewees," is a comprehensive field guide for identifying North American flycatchers. "Reflections: What Wildlife Needs and How to Provide it" by Dr. Mark Avery discusses wildlife issues in Britain and proposes remedies for protection. Lastly, "The Atlas of Early Modern Wildlife: Britain and Ireland between the Middle Ages and the Industrial Revolution" provides information on the historical distribution of animals in Britain and Ireland. These books offer valuable insights for researchers and bird enthusiasts interested in these topics. [Extracted from the article]

The article discusses a study, published in Science by Jeppe H. Rasmussen, Dan Stowell, and Elodie F. Briefer, addressing the urgent need for effective tools to monitor endangered plant and animal species, with 28 percent of assessed species currently at risk of extinction. It advocate for the use of noninvasive techniques like automated monitoring and machine learning to study animal populations through their vocalizations.

The concept of a soundscape is found in both ecology and music studies. Nature soundscapes and soundscape compositions are analyzed by both disciplines, respectively, to understand their biological diversity and ecosystem functioning and to interpret their compositional structure. A major challenge for both disciplines is visualizing the information embedded in a large variety of soundscapes and to share it with different audiences, from non-professionals to experts. To analyze soundscapes, both disciplines have independently developed similarity visualizations. However, no attempt has been made yet to combine these two fields of research to improve our ecological and musical perception of environmental sounds through shared similarity analysis methods. In this paper, we introduce a new visualization tool, the soundscape chord diagram (SCD), a circular similarity representation method that can be applied to any type of soundscape, either in ecoacoustics or electroacoustic studies. Our approach consists of visualizing spectral similarities between predefined sound segments based on the computation of a b-diversity acoustic index and on automatic clustering. SCDs were tested on two ecoacoustic forest databases and two electroacoustic soundscape compositions. SCDs were performant for the identification of specific acoustic events and highlighted known diel periods for nature soundscapes and written parts for soundscape compositions. This new visualization tool allows us to easily decipher the structure of musical and ecological acoustic data. SCDs could be applied to a large variety of soundscapes and promote their knowledge and preservation. This study opens a new way of investigating soundscapes at the interface between ecology and music, bringing together science and the arts. [ABSTRACT FROM AUTHOR]

Deep learning models that require vast amounts of training data struggle to achieve good animal sound classification (ASC) performance. Among recent few-shot ASC methods to address the data shortage problem regarding animals that are difficult to observe, model-agnostic meta-learning (MAML) has shown new possibilities by encoding common prior knowledge derived from different tasks into the model parameter initialization of target tasks. However, when the knowledge on animal sounds is difficult to generalize due to its diversity, MAML exhibits poor ASC performance due to the static initialization setting. In this paper, we propose a novel task-adaptive parameter transformation scheme called few-shot ASC. TAPT generates transformation variables while learning common knowledge and uses the variables to make parameters specific to the target task. Owing to this transformation, TAPT can reduce overfitting and enhance adaptability, training speed, and performance in heterogeneous tasks compared to MAML. In experiments on two public datasets on the same backbone network, we show that TAPT outperforms the existing few-shot ASC schemes in terms of classification accuracy, and in particular a performance improvement of 20.32% compared to the state-of-the-art scheme. In addition, we show that TAPT is robust to hyperparameters and efficient for training. [ABSTRACT FROM AUTHOR]

The article discusses the book "Sound Effect: The Theatre We Hear" by Ross Brown, which explores the history and significance of sound in theater and other forms of media. The author praises Brown's comprehensive and insightful approach, as he covers various topics such as the cultural influence on hearing, the use of sound in theater and film, and the social construction of sound. Brown also examines the ethical and societal implications of sound, including its potential for sexism and racism. Overall, the book offers a valuable perspective on the role of sound in our lives and is accessible to both academics and general readers interested in the subject. [Extracted from the article]

This paper describes an unusual underwater biological chorus recorded in the Southern California Bight and presents a numerical modeling approach that replicates aspects of the chorus. During one experiment, the evolution of the directionality of the chorusing region over time is suggestive of “The Wave”, the human waves performed by fans periodically standing and sitting in sports stadia around the world; here, the region of chorusing periodically propagated upcoast over 20 km of coastline at nearly 1.5 km/s. The chorus occurs predominantly at night in spring and summer, mostly in very shallow waters near the coast. It increases the underwater sound field levels within chorusing regions by 20–30 dB in the 50 Hz to 1 kHz frequency band. The chorus is composed of three parts; 1) a “sunset chorus” which is a 20-to-30 min continuous roar around sunset with received spectral levels up to 100 dB re 1 μPa2/Hz, 2) a “sunrise chorus” of lower level than the sunset chorus, and 3) an all-night-long cycling chorus. The cycling portion is made up of 15-to-20 s periods of higher received spectral levels (up to 90 dB re 1 μPa2/Hz within the chorusing region) each followed by a 10-to-20 s lull in which the spectral levels drop by 4–10 dB. This alternating pattern repeats every 30–40 s throughout the night. The numerical modeling approach is based on the physics of excitable media. A cellular automaton is used to model the two-dimensional spatial grid occupied by the calling animals (units), with each unit being either in the “resting”, “excitable”, or “active (calling)" state at each time step. Transition from resting to excitable and from active back to resting occurs automatically after a fixed period of time in the present state, whereas the probability of transitioning from excitable to active is determined not only by the elapsed time since entering the excitable state, but also by the received sound level at the unit location, creating a non-linear acoustics-based coupling between units. With appropriate inputs, many determined from measurements of the chorus properties and the individual animal calls themselves, simulations with the model can replicate the cycling levels in the night-long chorus, the continuous din of the sunset chorus, and (once properly initialized) the periodic upcoast evolution of the chorusing region (“The Wave”). When noise from a transiting ship is included in the simulations, the spatiotemporal characteristics of the chorus change appreciably, in ways similar to changes observed during the experiment.

Highlights: A humanoid intelligent display platform (HIDP) is created using stretchable and resilient ionotronic materials, and can be applicable in extreme environments and complex mechanical stimulations. HIDP links sound amplitude and brightness through machine learning for audiovisual interaction. HIDP identifies and displays animal species and corresponding frequencies in real-time. This study proposes a rational strategy for the design, fabrication and system integration of the humanoid intelligent display platform (HIDP) to meet the requirements of highly humanized mechanical properties and intelligence for human–machine interfaces. The platform's sandwich structure comprises a middle light-emitting layer and surface electrodes, which consists of silicon elastomer embedded with phosphor and silk fibroin ionoelastomer, respectively. Both materials are highly stretchable and resilient, endowing the HIDP with skin-like mechanical properties and applicability in various extreme environments and complex mechanical stimulations. Furthermore, by establishing the numerical correlation between the amplitude change of animal sounds and the brightness variation, the HIDP realizes audiovisual interaction and successful identification of animal species with the aid of Internet of Things (IoT) and machine learning techniques. The accuracy of species identification reaches about 100% for 200 rounds of random testing. Additionally, the HIDP can recognize animal species and their corresponding frequencies by analyzing sound characteristics, displaying real-time results with an accuracy of approximately 99% and 93%, respectively. In sum, this study offers a rational route to designing intelligent display devices for audiovisual interaction, which can expedite the application of smart display devices in human–machine interaction, soft robotics, wearable sound-vision system and medical devices for hearing-impaired patients. [ABSTRACT FROM AUTHOR]

Imagine you are focusing on the traffic on a busy street to ride your bike safely when suddenly you hear the siren of an ambulance. This unexpected sound involuntarily captures your attention and interferes with ongoing performance. We tested whether this type of distraction involves a spatial shift of attention. We measured behavioral data and magnetoencephalographic alpha power during a cross‐modal paradigm that combined an exogenous cueing task and a distraction task. In each trial, a task‐irrelevant sound preceded a visual target (left or right). The sound was usually the same animal sound (i.e., standard sound). Rarely, it was replaced by an unexpected environmental sound (i.e., deviant sound). Fifty percent of the deviants occurred on the same side as the target, and 50% occurred on the opposite side. Participants responded to the location of the target. As expected, responses were slower to targets that followed a deviant compared to a standard. Crucially, this distraction effect was mitigated by the spatial relationship between the targets and the deviants: responses were faster when targets followed deviants on the same versus different side, indexing a spatial shift of attention. This was further corroborated by a posterior alpha power modulation that was higher in the hemisphere ipsilateral (vs. contralateral) to the location of the attention‐capturing deviant. We suggest that this alpha power lateralization reflects a spatial attention bias. Overall, our data support the contention that spatial shifts of attention contribute to deviant distraction. Cognitive effects and the neural underpinnings accounting for deviant distraction have been extensively studied via event‐related potentials and behavioral measures. By focusing on neural oscillatory activity, our research advances our understanding on those aspects. It emphasizes the role of alpha power in the context of deviant distraction. Furthermore, it supports the hypothesis that spatial shifts of attention contribute to deviant distraction. [ABSTRACT FROM AUTHOR]

Animal cognition and ethology, the scientific study of animal behaviour, have long captivated the human imagination. Leave-taking behaviour in wild chacma baboons [[24]] was investigated using video footage and multivariate analysis, which do not require direct contact with the animals as for the use of drones in studying multilevel animal societies, while groundbreaking, does not compromise animal welfare. The Role of Cutting-Edge Technologies In recent years, a wave of technological innovation has revolutionised the field of ethology and animal cognition, propelling our understanding of non-human minds and behaviours to new heights. Crucially, the paradigm in animal cognition and ethology has shifted from a purely observational stance to one that recognises the animal agency [[13]]. [Extracted from the article]

Population declines and range contractions due to habitat loss are pervasive among nonhuman primates, with 60% of species threatened with extinction. However, the extensive vocal activity displayed by many primates makes them excellent candidates for passive acoustic surveys. Passive acoustic survey data is increasingly being used to support occupancy models, which have proven to be an efficient means of estimating both population trends and distributions. Passive acoustic surveys can be conducted relatively quickly and at broad scales, but efficient audio data processing has long proven elusive. The machine learning algorithm BirdNET was originally developed for birds but was recently expanded to include nonavian taxa. We demonstrate that BirdNET can accurately and efficiently identify an endangered primate, the Yucatán black howler monkey (Alouatta pigra), by sound in passive acoustic survey data (collected in southeastern Chiapas, Mexico), enabling us to use a single‐season occupancy model to inform further survey efforts. Importantly, we also generated data on up to 286 co‐occurring bird species, demonstrating the value of integrated animal sound classification tools for biodiversity surveys. BirdNET is freely available, requires no computer science expertise to use, and can readily be expanded to include more species (e.g., its species list recently tripled to >3000), suggesting that passive acoustic surveys, and thus occupancy modeling, for primate conservation could rapidly become much more accessible. Importantly, the long history of bioacoustics in primate research has yielded a wealth of information about their vocal behavior, which can facilitate appropriate survey design and data interpretation. Highlights: The BirdNET algorithm was developed to identify birds by sound but was recently expanded to include non‐avian taxa; we demonstrate that it can identify an endangered primate, the Yucatán black howler monkey (Alouatta pigra).BirdNET‐based howler monkey detections enabled us to use an occupancy model to inform further survey efforts—and to generate data on up to 286 co‐occurring bird species, illustrating the value of multispecies classifiers for biodiversity surveys.Combining free and easy‐to‐use tools like BirdNET, passive acoustic surveys, and occupancy modeling can facilitate rapid, broad‐scale assessments of primate population trends and distributions. [ABSTRACT FROM AUTHOR]

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Recordings of animal sounds enable a wide range of observational inquiries into animal communication, behavior, and diversity. Automated labeling of sound events in such recordings can improve both throughput and reproducibility of analysis. Here, we describe our software package for labeling elements in recordings of animal sounds, and demonstrate its utility on recordings of beetle courtships and whale songs. The software, DISCO, computes sensible confidence estimates and produces labels with high precision and accuracy. In addition to the core labeling software, it provides a simple tool for labeling training data, and a visual system for analysis of resulting labels. DISCO is open-source and easy to install, it works with standard file formats, and it presents a low barrier of entry to use. [ABSTRACT FROM AUTHOR]

The themes of medieval literature prevent the penetration not only of separate lexemes, closely related to the everyday life of the Bulgarians into the literary Old Bulgarian language but sometimes this affects entire classes of words. Onomatopoeias are one such class. The question of these words is completely unexplored historically. One of the stories in the Slavonic translation of the Dialogues of St. Gregory the Great allows for observations on the ways in which three times in Old Bulgarian literature an adequate translation was sought for the animal sounds with which Satan tried to drive the bishop of Milan out of his house. [ABSTRACT FROM AUTHOR]

Inattentional unawareness potentially occurs in several different sensory domains but is mainly described in visual paradigms ("inattentional blindness"; e.g., Simons & Chabris, 1999, Perception, 28, 1059–1074). Dalton and Fraenkel (2012, Cognition, 124, 367–372) were introducing "inattentional deafness" by showing that participants missed by 70% a voice repeatedly saying "I'm a Gorilla" when focusing on a primary conversation. The present study expanded this finding from the acoustic domain in a multifaceted way: First, we extended the validity perspective by using 10 acoustic samples—specifically, excerpts of popular musical pieces from different music genres. Second, we used as the secondary acoustic signal animal sounds. Those sounds originate from a completely different acoustic domain and are therefore highly distinctive from the primary sound. Participants' task was to count different musical features. Results (N = 37 participants) showed that the frequency of missed animal sounds was higher in participants with higher attentional focus and motivation. Additionally, attentional focus, perceptual load, and feature similarity/saliency were analyzed and did not have an influence on detecting or missing animal sounds. We could demonstrate that for 31.2% of the music plays, people did not recognize highly salient animal voices (regarding the type of acoustic source as well as the frequency spectra) when executing the primary (counting) task. This uncovered, significant effect supports the idea that inattentional deafness is even available when the unattended acoustic stimuli are highly salient. [ABSTRACT FROM AUTHOR]

Long‐term vegetation plots represent one of the largest types of research investments in ecology, but efforts to interrelate data on plants with that on animals are constrained because of the disturbance produced by human observers. Recent advances in the automated identification of animal sounds on large datasets of autonomously collected audio recordings hold the potential to describe plant–animal interactions, such as between frugivorous birds and fruiting trees, without such disturbance. We deployed an array of nine autonomous recording units (ARUs) on the 400 × 500 m Bubeng Forest Dynamics Plot, in Xishuangbanna, southwest China, and collected a total of 1965 h of recordings across two seasons. Animal Sound Identifier (ASI) software was used to detect the vocalizations of five frugivorous bird species, and the probability of detection was related to the number of mature fruiting trees within a 50 m radius of the ARUs. There were more significant positive relationships than would be expected by chance in analyses that investigated bird/tree interactions across 3 months, both in the wet season and the dry season, as well as in short‐term analyses within the dry season months of October and November. The analysis identified 54 interactions between bird and tree species with significant positive relationships. Follow‐up observations of birds on the plot validated that such interactions were more likely to be observed than other interactions. We demonstrate that ARUs and automated voice identification can map the distribution and/or movement of vocal animals across large vegetation plots, allowing this data on animals to be inter‐related to that on plants. We suggest that ARUs be added to the standardized protocols of the plot network, leveraging their vast amount of information about vegetation to describe plant–animal interactions currently, and monitor changes in the future. [ABSTRACT FROM AUTHOR]

Excellent performance has been demonstrated in implementing challenging agricultural production processes using modern information technology, especially in the use of artificial intelligence methods to improve modern production environments. However, most of the existing work uses visual methods to train models that extract image features of organisms to analyze their behavior, and it may not be truly intelligent. Because vocal animals transmit information through grunts, the information obtained directly from the grunts of pigs is more useful to understand their behavior and emotional state, which is important for monitoring and predicting the health conditions and abnormal behavior of pigs. We propose a sound classification model called TransformerCNN, which combines the advantages of CNN spatial feature representation and the Transformer sequence coding to form a powerful global feature perception and local feature extraction capability. Through detailed qualitative and quantitative evaluations and by comparing state-of-the-art traditional animal sound recognition methods with deep learning methods, we demonstrate the advantages of our approach for classifying domestic pig sounds. The scores for domestic pig sound recognition accuracy, AUC and recall were 96.05%, 98.37% and 90.52%, respectively, all higher than the comparison model. In addition, it has good robustness and generalization capability with low variation in performance for different input features. [ABSTRACT FROM AUTHOR]

Animal sound classification (ASC) refers to the automatic identification of animal categories by sound, and is useful for monitoring rare or elusive wildlife. Thus far, deep-learning-based models have shown good performance in ASC when training data is sufficient, but suffer from severe performance degradation if not. Recently, generative adversarial networks (GANs) have shown the potential to solve this problem by generating virtual data. However, in a multi-class environment, existing GAN-based methods need to construct separate generative models for each class. Additionally, they only consider the waveform or spectrogram of sound, resulting in poor quality of the generated sound. To overcome these shortcomings, we propose a two-step sound augmentation scheme using a class-conditional GAN. First, common features are learned from all classes of animal sounds, and multiple classes of animal sounds are generated based on the features that consider both waveforms and spectrograms using class-conditional GAN. Second, we select data from the generated data based on the confidence of the pretrained ASC model to improve classification performance. Through experiments, we show that the proposed method improves the accuracy of the basic ASC model by up to 18.3%, which corresponds to a performance improvement of 13.4% compared to the second-best augmentation method. [ABSTRACT FROM AUTHOR]

To disguise man-made communications as natural signals, underwater transceivers have the option to pre-record animal vocalizations, and play them back in a way that carries meaningful information for a trained receiver. This operation, known as biomimicking, has been used to perform covert communications and to emit broadband signals for localization, either by playing pre-recorded animal sounds back into the environment, or by designing artificial waveforms whose spectrum is close to that of bioacoustic sounds.However, organic sound-emitting body structures in animals have very different trans-characteristics with respect to electro-acoustic transducers used in underwater acoustic transceivers. In this paper, we observe the distortion induced by transmitting pre-recorded animal vocalization through a transducer's front-end, and argue that such distortion can be detected via appropriate entropy metrics. We test ten different metrics for this purpose, both via emulated transmission and in two field experiments. Our result indicate which signals and entropy metrics lead to the highest probability of detecting transducer-originated distortions, thus exposing ongoing covert communications. Our research emphasizes the limitations that man-made equipment incurs when reproducing bioacoustic sounds, and prompts for the choice of biomimicking signals that are possibly suboptimal for communications or localization, but help avoid exposing disguised transmissions. [ABSTRACT FROM AUTHOR]

The present study aims to present and compare the sounds produced by the representatives of the family Canidae, in particular the genus Canis, in terms of their amplitude and frequency characteristics. The results show that the power generated by the vocal apparatus of wolves is the greatest, compared to that of the domestic dog and the jackal, in which they are the lowest, presented in dB. The reflected values for the frequency characteristic of the sound signals in these three representatives of the genus Canis are inversely related - highest in jackals, again average in dogs and lowest in wolves. [ABSTRACT FROM AUTHOR]