Avian Vocalizations Research Articles

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.

AVN: A Deep Learning Approach for the Analysis of Birdsong.

Deep learning tools for behavior analysis have enabled important new insights and discoveries in neuroscience. Yet, they often compromise interpretability and generalizability for performance, making it difficult to quantitively compare phenotypes across datasets and research groups. We developed a novel deep learning-based behavior analysis pipeline, Avian Vocalization Network (AVN), for the learned vocalizations of the most extensively studied vocal learning model species - the zebra finch. AVN annotates songs with high accuracy across multiple animal colonies without the need for any additional training data and generates a comprehensive set of interpretable features to describe the syntax, timing, and acoustic properties of song. We use this feature set to compare song phenotypes across multiple research groups and experiments, and to predict a bird's stage in song development. Additionally, we have developed a novel method to measure song imitation that requires no additional training data for new comparisons or recording environments, and outperforms existing similarity scoring methods in its sensitivity and agreement with expert human judgements of song similarity. These tools are available through the open-source AVN python package and graphical application, which makes them accessible to researchers without any prior coding experience. Altogether, this behavior analysis toolkit stands to facilitate and accelerate the study of vocal behavior by enabling a standardized mapping of phenotypes and learning outcomes, thus helping scientists better link behavior to the underlying neural processes.

Improving learning-based birdsong classification by utilizing combined audio augmentation strategies

In ecology, changes in environmental conditions are often closely linked to shifts in species diversity. This relationship can be investigated by analyzing avian vocalizations, which are robust indicators of trends in biodiversity. Within this contribution, we explored various data augmentation techniques and deep learning strategies for the classification of birdsong within natural soundscapes. For this purpose, we employed three fundamental deep neural network architectures, such as vision transformers, to classify 397 different bird species. To improve both the accuracy and generalizability of our models, we incorporated up to 19 well-established data augmentation techniques commonly used in audio classification. This included an iterative selection process where only augmentations that enhanced classification performance were selected. The primary augmentation technique involved the integration of various noise samples and non-bird audio elements, which significantly improved model performance as assessed on the BirdCLEF 2021 data set. Individual augmentations achieved F1-scores from 48.0 % (vertical flip) to 72.6 % (primary background noise soundscapes). Through the strategic combination of key techniques – namely simulated pink noise, interspecies sound mixing, and loudness normalization – we achieved a top F1-score of 73.7%. Depending on the selected classification model, this corresponds to an improvement by 4.81 % to 10.5 %. Improvements and deteriorations of all applied augmentation techniques appeared to be robust across our three evaluated models. Therefore, our approach highlights the potential of sophisticated audio augmentations in refining the accuracy and robustness of birdsong classification models.

Aggregated time‐series features boost species‐specific differentiation of true and false positives in passive acoustic monitoring of bird assemblages

AbstractPassive acoustic monitoring (PAM) has gained increasing popularity to study behaviour, habitat preferences, distribution and community assembly of birds and other animals. Automated species classification algorithms like ‘BirdNET’ are capable of detecting and classifying avian vocalizations within extensive audio data, covering entire species assemblages. PAM reveals substantial potential for biodiversity monitoring that informs evidence‐based conservation. Nevertheless, fully realizing this potential remains challenging, especially due to the issue of false‐positive species detections. Here, we introduce an optimized thresholding framework, which incorporates contextual information extracted from the time‐series of automated species detections (i.e. covariates on quality and quantity of species' detections measured at varying time intervals) to improve the differentiation of true and false positives. We verified a sample of BirdNET detections per species and modelled species‐specific thresholds using conditional inference trees. These thresholds were designed to minimize false‐positive detections while maximizing the preservation of true positives in the dataset. We tested this framework for a large dataset of BirdNET detections (5760 h of audio data, 60 sites) recorded over an entire breeding season. Our results revealed considerable interspecific variability of precision (percentage of true positives) within raw BirdNET data. Our optimized thresholding approach achieved high precision (≥0.9) for 70% of the 61 detected species, while species‐specific thresholds solely relying on the BirdNET confidence scores achieved high precision for only 31% of the species. Conservative universal thresholds (not species‐specific) reached high precision for 48% of the species. Our thresholding approach outperformed previous thresholding approaches and enhanced interspecific comparability for bird community analyses. By incorporating contextual information from the time‐series of species detections, the differentiation of true and false positives was substantially improved. Our approach may enhance a straightforward application of PAM in biodiversity research, landscape planning and evidence‐based conservation.

A novel deep learning‐based bioacoustic approach for identification of look‐alike white‐eye (Zosterops) species traded in wildlife markets

The songbird trade crisis in East and South East Asia has been fuelled by high demand, driving many species to the brink of extinction. This demand, driven by the desire for songbirds as pets, for singing competitions and for prayer animal release has led to the overexploitation of numerous species and the introduction and spread of invasive alien species and diseases to novel environments. The ability to identify traded species efficiently and accurately is crucial for monitoring bird trade markets, protecting threatened species and enforcing wildlife laws. Citizen scientists can make major contributions to these conservation efforts but may be constrained by difficulties in distinguishing ‘look‐alike’ bird species traded in markets. To address this challenge, we developed a novel deep learning‐based Artificial Intelligence (AI) bioacoustic tool to enable citizen scientists to identify bird species traded in markets. To this end, we used three major avian vocalization databases to access bioacoustic data for 15 morphologically similar White‐eye (Zosterops) species that are commonly traded in Asian wildlife markets. Specifically, we employed the Inception v3 pre‐trained model to classify the 15 White‐eye species and ambient sound (i.e. non‐bird sound) using 448 bird recordings we obtained. We converted recordings into spectrogram (i.e. image form) and used eight image augmentation methods to enhance the performance of the AI neural network through training and validation. We found that recall, precision and F1 score increased as the amount of data augmentation increased, resulting in up to 91.6% overall accuracy and an F1 score of 88.8% for identifying focal species. Through the application of bioacoustics and deep learning, this approach would enable citizen scientists and law enforcement officials efficiently and accurately to identify prohibited trade in threatened species, making important contributions to conservation.

Homology and the evolution of vocal folds in the novel avian voice box

The origin of novel traits, those that are not direct modifications of a pre-existing ancestral structure, remains a fundamental problem in evolutionary biology. For example, little is known about the evolutionary and developmental origins of the novel avian vocal organ, the syrinx. Located at the tracheobronchial junction, the syrinx is responsible for avian vocalization, but it is unclear whether avian vocal folds are homologous to the laryngeal vocal folds in other tetrapods or convergently evolved. Here, we identify a core developmental program involved in avian vocal fold formation and infer the morphology of the syrinx of the ancestor of modern birds. We find that this ancestral syrinx had paired sound sources induced by a conserved developmental pathway and show that shifts in these signals correlate with syringeal diversification. We show that, despite being derived from different developmental tissues, vocal folds in the syrinx and larynx have similar tissue composition and are established through a strikingly similar developmental program, indicating that co-option of an ancestral developmental program facilitated the origin of vocal folds in the avian syrinx.

Snowmobile noise alters bird vocalization patterns during winter and pre‐breeding season

Abstract Noise pollution poses a significant threat to ecosystems worldwide, disrupting animal communication and causing cascading effects on biodiversity. In this study, we focus on the impact of snowmobile noise on avian vocalizations during the non‐breeding winter season, a less‐studied area in soundscape ecology. We developed a pipeline relying on deep learning methods to detect snowmobile noise and applied it to a large acoustic monitoring dataset collected in Yellowstone National Park. Our results demonstrate the effectiveness of the snowmobile detection model in identifying snowmobile noise and reveal an association between snowmobile passage and changes in avian vocalization patterns. Snowmobile noise led to a decrease in the frequency of bird vocalizations during mornings and evenings, potentially affecting winter and pre‐breeding behaviours such as foraging, predator avoidance and successfully finding a mate. However, we observed a recovery in avian vocalizations after detection of snowmobiles during mornings and afternoons, indicating some resilience to sporadic noise events. Synthesis and applications: Our findings emphasize the need to consider noise impacts in the non‐breeding season and provide valuable insights for natural resource managers to minimize disturbance and protect critical avian habitats. The deep learning approach presented in this study offers an efficient and accurate means of analysing large‐scale acoustic monitoring data and contributes to a comprehensive understanding of the cumulative impacts of multiple stressors on avian communities.

Ostrich (Struthio camelus) syrinx morphology and vocal repertoire across postnatal ontogeny and sex: Implications for understanding vocal evolution in birds.

Vocal production in birds has been the target of considerable research that mostly has focused on phylogenetically well-nested songbirds. Anatomical descriptions and recordings of many non-songbirds have often only focused on a single ontogenetic stage or sex. While basic morphology of the vocal organ (syrinx) of ostrich (Palaeognathae, Struthio camelus) has been known since the 1800s, descriptions of its vocal repertoire and syrinx anatomy since then have been incomplete or inconsistent. New toolkits now enable detailed qualitative description of internal anatomy and meristic data and allow it to be compared to vocal production. Here we describe the anatomy of the syrinx in Struthio camelus for three post-hatching ontogenetic stages and both an adult male and female utilizing dissection and contrast enhanced X-ray computed tomography (diceCT). We find changes in ring geometry and spacing through ontogeny as well as lateral labia thickness. We document a small unpaired, midline, cartilaginous structure, a "pessuliform process" at the tracheobronchial juncture present throughout ontogeny and in both males and females. Investigation of the vocal repertoire of ostriches across ontogeny using a new dataset of 77 recordings led to identification of four vocalizations not previously reported in the literature, including the simultaneous production of a hiss and tonal. We find syrinx morphology largely consistent across ontogeny and in male and female adults. Both are capable of producing long duration tonal calls, but these may be more frequent in male birds. Closed-mouth boom calls remain unique to males. A detailed understanding of diversity in parts of early diverging clades is pivotal in attempting to estimate features of the ancestral syrinx in birds and how avian vocalization evolved.

Automatic Birdsong Splitting and Syllabic Analysis of Jalak Suren

The study of birdsong has received relatively limited attention in the field of artificial intelligence, despite its long-standing intrigue and the question of whether birds possess a form of language. Previous research has provided evidence suggesting the presence of structurally organized words recognized by birds, such as the strong reactions observed in Japanese tits and Pied babblers when exposed to specific sequences of artificially played calls. Altering the speed of a sequence also influences the birds' responses, further supporting the existence of organized linguistic units in avian vocalizations. In this study, we propose a novel approach for analyzing birdsong by employing automatic syllable segmentation and syllabic similarity analysis. Our focus is on the Jalak Suren species (Sturnus contra), renowned for its melodious song. Through the identification and categorization of distinct syllabic units in birdsong recordings, we investigate the statistical occurrence of these syllables within the sequence of birdsong. Our findings reveal remarkable similarities between the statistical occurrence of syllables in birdsong and those found in human language passages

A survey of vocal mimicry in companion parrots

Parrots are one of the rare animal taxa with life-long vocal learning. Parrot vocal repertoires are difficult to study in the wild, but companion parrots offer a valuable data source. We surveyed the public about mimicry repertoires in companion parrots to determine whether vocal learning varied by (1) species, (2) sex, (3) age, and (4) social interaction with other parrots. Species differed significantly in mimicry ability, with grey parrots (Psittacus erithacus) having the largest mimicry repertoires. Analyses of all birds (n = 877) found no overarching effects of sex, age, or parrot-parrot social interactions on mimicry repertoires. Follow up analyses (n = 671), however, revealed a human bias to assume that talking parrots are male, and indicated that five of the 19 best-sampled species exhibited sex differences. Age-specific analyses of grey parrots (n = 187) indicated that repertoire size did not increase during adulthood. Most parrots were capable of improvisation (e.g. rearranging words) and used mimicry in appropriate human contexts. Results indicate that parrot vocal production learning varies among and within species, suggesting that the mechanisms and functions of learning also vary. Our data provide a rich foundation for future comparative research on avian vocalizations, and broaden our understanding of the underpinnings of communicative behavior and learning across all animals.

Diel and seasonal variation of Striped Cuckoo (Tapera naevia) vocalizations revealed using automated signal recognition

Studying seasonal changes in the vocal activity of birds may shed light on the function of avian vocalizations and the phenology of life history events. Our current knowledge regarding the seasonality of the vocal behaviour of tropical birds in general, and avian brood parasites in particular, is very limited. Here, we employed passive acoustic monitoring with automated signal recognition to monitor the vocal behaviour of the Striped Cuckoo Tapera naevia over a complete annual cycle in the Brazilian Pantanal. We evaluated whether the pattern of vocal activity differed between the two main vocalizations of the species (sem‐fim and wee‐series songs) and whether vocal activity varied with time of day and season. The diel pattern of vocal activity for both vocalization types showed a bimodal pattern, with the first peak of vocal activity in the few hours after sunrise and the second peak before sunset, in agreement with prior studies in other cuckoos. The sem‐fim song was also produced during the night, while the wee‐series song was produced only during the day. Both vocalization types showed clear seasonality and were produced between mid‐June and mid‐February. Seasonal changes in vocal activity suggest that the Striped Cuckoo breeds during the dry season and leaves the study area during the flooding period. The seasonal pattern of the wee‐series song showed strong seasonality, with 90% of these calls detected in September and October, whereas the sem‐fim song showed weaker seasonality, with 80% of the calls detected during the July–October period. Our study indicates that automated signal recognition might be a reliable tool for monitoring cuckoos. Further research could evaluate whether the different seasonal patterns of the sem‐fim and wee‐series songs of the Striped Cuckoo are related to different functions.

Importance of sleep for avian vocal communication.

Sleep is one of the few truly ubiquitous animal behaviours, and though many animals spend enormous periods of time asleep, we have only begun to understand the consequences of sleep disturbances. In humans, sleep is crucial for effective communication. Birds are classic models for understanding the evolution and mechanisms of human language and speech. Bird vocalizations are remarkably diverse, critical, fitness-related behaviours, and the way sleep affects vocalizations is likely similarly varied. However, research on the effects of sleep disturbances on avian vocalizations is shockingly scarce. Consequently, there is a critical gap in our understanding of the extent to which sleep disturbances disrupt communication. Here, we argue that sleep disturbances are likely to affect all birds' vocal performance by interfering with motivation, memory consolidation and vocal maintenance. Further, we suggest that quality sleep is likely essential when learning new vocalizations and that sleep disturbances will have especially strong effects on learned vocalizations. Finally, we advocate for future research to address gaps in our understanding of how sleep influences vocal learning and performance in birds.

Analytical approaches for evaluating passive acoustic monitoring data: A case study of avian vocalizations.

The interface between field biology and technology is energizing the collection of vast quantities of environmental data. Passive acoustic monitoring, the use of unattended recording devices to capture environmental sound, is an example where technological advances have facilitated an influx of data that routinely exceeds the capacity for analysis. Computational advances, particularly the integration of machine learning approaches, will support data extraction efforts. However, the analysis and interpretation of these data will require parallel growth in conceptual and technical approaches for data analysis. Here, we use a large hand‐annotated dataset to showcase analysis approaches that will become increasingly useful as datasets grow and data extraction can be partially automated.We propose and demonstrate seven technical approaches for analyzing bioacoustic data. These include the following: (1) generating species lists and descriptions of vocal variation, (2) assessing how abiotic factors (e.g., rain and wind) impact vocalization rates, (3) testing for differences in community vocalization activity across sites and habitat types, (4) quantifying the phenology of vocal activity, (5) testing for spatiotemporal correlations in vocalizations within species, (6) among species, and (7) using rarefaction analysis to quantify diversity and optimize bioacoustic sampling.To demonstrate these approaches, we sampled in 2016 and 2018 and used hand annotations of 129,866 bird vocalizations from two forests in New Hampshire, USA, including sites in the Hubbard Brook Experiment Forest where bioacoustic data could be integrated with more than 50 years of observer‐based avian studies. Acoustic monitoring revealed differences in community patterns in vocalization activity between forests of different ages, as well as between nearby similar watersheds. Of numerous environmental variables that were evaluated, background noise was most clearly related to vocalization rates. The songbird community included one cluster of species where vocalization rates declined as ambient noise increased and another cluster where vocalization rates declined over the nesting season. In some common species, the number of vocalizations produced per day was correlated at scales of up to 15 km. Rarefaction analyses showed that adding sampling sites increased species detections more than adding sampling days.Although our analyses used hand‐annotated data, the methods will extend readily to large‐scale automated detection of vocalization events. Such data are likely to become increasingly available as autonomous recording units become more advanced, affordable, and power efficient. Passive acoustic monitoring with human or automated identification at the species level offers growing potential to complement observer‐based studies of avian ecology.

Hierarchical classification of multi-corpus acoustic field recordings

Passive acoustic monitoring is one important technique for detecting long-term ecological change. Since audio data contains complex temporal and frequency information, deep learning is an ideal computational framework for automatic processing. In this work, the dataset consists of 64k annotated clips distributed across 23 classes that are derived from 17 000 h of acoustic data consisting of bio-, geo- and anthropophonies recorded at two different sites in the Capital Region of New York State. A hierarchical deep convolutional neural network with one local classifier per parent node provides fine, medium, and coarse-grained label predictions. If a fine-grained label is predicted incorrectly, a correct medium or coarse label still provides valuable taxonomic information. While the flat classifier performs similarly to the hierarchical for single-corpus training scenarios, it achieves higher medium-grained predictions for categories such as avian vocalization. For cross-corpus scenarios, the hierarchical classifier achieves a superior performance overall. These results highlight the importance of neural network architecture customization to suit a particular task and domain; hierarchical structures show potential in open set recognition tasks, where the entire set of possible classes is unknown. [Work supported by NSF Grant No. 1631674 and an RPI HASS Fellowship.]

Eco-Acoustic Assessment of an Urban Park by Statistical Analysis

We investigated the statistical properties of the sounds recorded at a site located in the Parco Nord of Milan, Italy, characterized by wooded land rich in biodiversity and exposed to different sources and types of anthropogenic disturbances with the aim of deriving information on its environmental quality in terms of biophonic presence and athropic disturbance. A time series of eco-acosutic indices were determined for 616 audio files recorded from 30 April 2019 (5:00 p.m.) to 3 May 2019 (5:00 a.m.) with a 1-min duration followed by a 5-min pause (10 recordings per hour). In the present study, the following indices were computed: the Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Bio-acoustic Index (BI), Acoustic Entropy Index (H), Normalized Difference Soundscape Index (NSDI) and Dynamic Spectral Centroid (DSC). Cluster analysis performed on the corresponding time series yielded a dimensional reduction from seven down to three. The results show a clear separation of the eco-acoustic indices into two clusters, reflecting the different dynamics and diversity behaviour throughout the recordings. A post-processing aural survey was also performed, aiming at determining biophonic activities (mainly avian vocalization and other animals), the characteristics of technophonies sources (mainly road traffic noise and airplane fly-overs), human presence (voices and steps) and geophonies (rain and wind). The statistical analysis proved to be a robust tool due to the good matching obtained with the aural survey outcomes. The overall quality of the Parco Nord phonic activity was found to be low. Notwithstanding the presence of avian species, highlighted by the characteristic dawn chorus, both clusters revealed low “scores” of NDSI and DSC indices heavily influenced by road traffic sources. This study represents the first step toward the realization of maps of eco-acoustic indices for the long-term monitoring of fragile habitats.

Avian vocalisations: the female perspective.

Research on avian vocalisations has traditionally focused on male song produced by oscine passerines. However, accumulating evidence indicates that complex vocalisations can readily evolve outside the traditional contexts of mate attraction and territory defence by male birds, and yet the previous bias towards male song has shaped - and continues to shape - our understanding of avian communication as a whole. Accordingly, in this review we seek to address this imbalance by synthesising studies on female vocalisations from across signalling contexts throughout the Aves, and discuss the implications of recent empirical advances for our understanding of vocalisations in both sexes. This review reveals great structural and functional diversity among female vocalisations and highlights the important roles that vocalisations can play in mediating female-specific behaviours. However, fundamental gaps remain. While there are now several case studies that identify the function of female vocalisations, few quantify the associated fitness benefits. Additionally, very little is known about the role of vocal learning in the development of female vocalisations. Thus, there remains a pressing need to examine the function and development of all forms of vocalisations in female birds. In the light of what we now know about the functions and mechanisms of female vocalisations, we suggest that conventional male-biased definitions of songs and calls are inadequate for furthering our understanding of avian vocal communication more generally. Therefore, we propose two simple alternatives, both emancipated from the sex of the singer. The first distinguishes song from calls functionally as a sexually selected vocal signal, whilst the second distinguishes them mechanistically in terms of their underlying neurological processes. It is clear that more investigations are needed into the ultimate and proximate causes of female vocalisations; however, these are essential if we are to develop a holistic epistemology of avian vocal communication in both sexes, across ecological contexts and taxonomic divides.

Male lyrebirds create a complex acoustic illusion of a mobbing flock during courtship and copulation

Darwin argued that females' "taste for the beautiful" drives the evolution of male extravagance,1 but sexual selection theory also predicts that extravagant ornaments can arise from sexual conflict and deception.2,3 The sensory trap hypothesis posits that elaborate sexual signals can evolve via antagonistic coevolution whereby one sex uses deceptive mimicry to manipulate the opposite sex into mating.3 Here, the success of deceptive mimicry depends on whether it matches the receiver's percept of the model,4 and so has little in common with concepts of aesthetic judgement and 'beauty.'1,5-9 We report that during their song and dance displays,10 male superb lyrebirds (Menura novaehollandiae) create an elaborate acoustic illusion of a mixed-species mobbing flock. Acoustic analysis showed that males mimicked the mobbing alarm calls of multiple species calling together, enhancing the illusion by also vocally imitating the wingbeats of small birds. A playback experiment confirmed that this illusion was sufficient to fool avian receivers. Furthermore, males produced this mimicry only (1) when females attempted to exit male display arenas, and (2) during the lyrebirds' unusually long copulation, suggesting that the mimicry aims to prevent females from prematurely terminating these crucial sexual interactions. Such deceptive behavior by males should select for perceptual acuity in females, prompting an inter-sexual co-evolutionary arms race between male mimetic accuracy and discrimination by females. In this way the elaboration of the complex avian vocalizations we call 'song' could be driven by sexual conflict, rather than a female's preference for male extravagance.

The Relevance of Francolin Vocalization in Local Time Indication in Mamfe Municipality, Southwest Region, Cameroon

Avian vocalizations can have intra-specific and inter-specific functions such as communication alarm, resource location, pair-bond maintenance, territory defense, and mate attraction. Majority of studies have focused on the diurnal vocalization of birds. Very little research has been done on nocturnal vocalization behavior of birds such as francolins in Cameroon. For this reason, this study was aimed at assessing the importance of francolin vocalization in local time indication to the inhabitants of Mamfe municipality. The data collection method of this study comprised of random spot sampling of the francolin number, recording vocalizations in the dawn and dusk, and visiting the francolin nesting sites. Surveys were conducted 5 days per week, from Monday to Friday for a period of one month. In the dawn, call recording started 7:00am and ended 5:00pm, while in the dusk it started 7:00 pm and ended 6:00am. This study revealed a significant association between francolin number and the day-period, χ 2 = 19.219 df=6 P=0.004. Vocalization time and frequency showed a significant link, χ 2 = 6.834 df=8 P<0.05. Similar results were obtained from the association of bird number and vocalization frequency, χ 2 = 21.294 df=4 P=0.000. The bird number was proportionate to call frequency and intensity, 5 birds and above generated a call frequency of 64.79%. The day-period and bird number associated significantly, χ 2 = 51.278 df=44 P<0.05. Additionally, cropland vegetation showed a significant correlation on bird number, r = 0.244 P<0.05. Furthermore, the bird group-size witnessed an influence on the call location, with more calls from cropland (70.42%) than the forest vegetation (29.58%). The consistent vocalization frequency during the early hours of the morning period is useful to the local inhabitants of the municipality. Almost all the local crop-farmers interviewed acknowledged that the francolin vocalization were time-specific, hence, reminding them upon the preparation of early morning farming engagements. Moreover, it is believed in most part of Cameroon, especially in remote communities that the francolin vocalizations help to wake up local farmers from sleep during the early of the day. However, the declining population of this bird species raises many questions on its conservation. Keywords : Francolin vocalization, Communication alarm, Bird number, Cropland vegetation DOI: 10.7176/CER/12-9-03 Publication date: September 30 th 2020

A new soundscape analysis tool: Soundscape Analysis and Mapping System (SAMS)

There is currently considerable interest in the quantitative analysis tools of soundscape to derive supplemental information of a landscape. However, existing soundscape software for acoustic data analyses does not consider the geospatial characteristics of the landscape although they are essential components of soundscape ecology. The new software described in this paper, Soundscape Analysis and Mapping System (SAMS), allows users to quickly process and analyze large volumes of acoustic data in geospatial environment. Specifically, SAMS integrates soundscape data with landscape information and visualization on the same platform that can be directly retrieved from Google Earth. A soundscape case study, used as an example to demonstrate usefulness of the SAMS tool, revealed an obvious spatial heterogeneity of soundscape across the landscape. Further analyses suggested that the transformation of natural wetland ecosystems into various dispersed urban structures directly led to significant changes in soundscape properties, implying human-noise intrusion and reduction in avian vocalizations. Therefore, this new SAMS software offers a novel analytical tool for users, such as urban planners or acoustic ecologists, to assess the impact of spatial patterns on soundscape characteristics.

Chrétien the Jay: Avian Rhetoric in Philomena

I argue here that the medieval French Philomena, found exclusively within the Ovide moralisé, a fourteenth-century translation and adaptation of Ovid's Metamorphoses, adumbrates a theory of vocal repetition as an aesthetic that is shared by both birds and humans. Various common rhetorical devices that foreground acoustic reduplication appear in high density in the text, where they are often collocated with evocations of birdsong. After exploring the parallels between these devices and the structure of avian vocalization, I show the presence of several passages in the text that can be read simultaneously as onomatopoeic evocations of birdsong and as standard referential language. I also propose a solution to the mysterious label associated with the text's author, “Chrétien li gois” (“Chrétien the jay”).