Acoustic monitoring of wildlife in inaccessible areas and automatic detection of bird songs from continuous recordings

The use of new technology for wildlife monitoring comes with both possible benefits and challenges. Unmanned aerial vehicles (UAVs) and automatic recording units (ARUs) can allow researchers to automatically record videos, photographs, and audio recordings of animals in unusual or inaccessible locations. However, new acoustic monitoring techniques require innovative methods to extract and utilize data from acoustic recordings. In this project we developed novel technology to record bird songs in inaccessible areas and demonstrated a useful method for extracting and classifying songs from continuous recordings. The autonomous aerial acoustic recording system (AAARS) was a UAV developed at the University of Tennessee capable of generating high-quality WAV recordings of bird songs in a variety of landscapes. The AAARS was completely silent in flight controlled by a ground-based computer monitoring station. I developed a model to convert the AAARS GPS-based flight path into a microphone exposure surface to relate species-specific acoustic signals recorded to area of microphone coverage. The vocalizations per unit area per unit time for a given focal species could then be used as an index of relative abundance or as an input in density estimation. Once collected, extraction and classification of birdsongs from acoustic recordings remains a major technological challenge. I used quadratic discrimination analysis to differentiate between inter- and intra-specific bird songs using up to sixteen acoustic measurements on human-extracted signals from audio spectrograms of five focal songbird species. Measurement-based classification was successful at separating the five species apart with only ≤5% classification error. I then used a template-matching model to extract target birdsongs from continuous field recordings and investigated the efficiency of different analytical options for classification of five focal songbird species. Decision trees, neural networks, and quadratic discriminant analysis all produced similar classification results. The means to optimize the analytical approach varied by species. I concluded that a species-specific approach should be used to accurately extract and classify songs from continuous recordings

Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformers

We propose a shift towards end-to-end learning in bird sound monitoring by combining self-supervised (SSL) and deep active learning (DAL). Leveraging transformer models, we aim to bypass traditional spectrogram conversions, enabling direct raw audio processing. ActiveBird2Vec is set to generate high-quality bird sound representations through SSL, potentially accelerating the assessment of environmental changes and decision-making processes for wind farms. Additionally, we seek to utilize the wide variety of bird vocalizations through DAL, reducing the reliance on extensively labeled datasets by human experts. We plan to curate a comprehensive set of tasks through Huggingface Datasets, enhancing future comparability and reproducibility of bioacoustic research. A comparative analysis between various transformer models will be conducted to evaluate their proficiency in bird sound recognition tasks. We aim to accelerate the progression of avian bioacoustic research and contribute to more effective conservation strategies.Comment: Accepted @AI4S ECAI2023. This is the author's version of the wor

Number of syllables in cuckoo Cuculus canorus calls: A test using a citizen science project

International audienceRecent studies revealed that the call of the common cuckoo Cuculus canorus has more inter-individual than intra-individual variation and that the number of syllables depends on environmental conditions, but also the presence of male and female conspecifics. However, still very little is known about how song varies at a global scale, especially considering the wide distribution of this species across most of Europe and Asia. Xeno-canto.org is a vocalization repository for birdsong. We used xeno-canto.org as a data source for investigating the variables that affect the number of syllables in cuckoo calls at a large spatial scale. At a very broad geographical scale, the number of syllables in cuckoo calls predicted bird species richness. Additionally, female calls were associated with shorter males calls, and there was a positive correlation between the interaction between female calls and the number of host races parasitized by the cuckoo. These findings confirm that intraspecific and interspecific interactions significantly affect the number of syllables in cuckoo calls, and both environmental variables and biotic interactions should be considered in future studies of vocalizations in cuckoos. Last but not least, we demonstrated that a citizen science project is a useful source for ecological studies at large spatial scales

IDENTIFICATION OF BIRD SOUND AS THE TOOLS FOR ENVIRONMENTAL QUALITY MONITORING IN GREEN OPEN SPACE

The presence of a bird community can be used as a bioindicator of environmental quality in suburban areas. Bird identification from sound recording has developed in the last decade. This research aims to analyze the quality of green open space in the suburban area of Sleman Regency based on bird acoustic analysis. Data collection was carried out from November 2020 to March 2021 on river borders and city parks. Bird sound data was carried out using a mobile phone, facilitated by the Arbimon Touch application, in the morning, afternoon, and evening at intervals of five minutes on and ten minutes off for five days. The sound recordings and spectrograms were identified and validated through the database on the xeno-canto website. The identified birds are then used to calculate the Bird Community Index (BCI). The analysis of sound recordings found 29 bird species from 18 families in the sampling location. In addition, four birds were recorded as vulnerable or protected. The Progo River Border is the green open space with the highest species number of birds, but the Kayen river border and the Taman Keanekaragaman Hayati dan Arboretum Bambu have the best environmental quality to the presence of the higher specialist bird. Based on the bird community index, the environmental quality of the GOS in the suburban area of Sleman Regency has a moderate to very low level of environmental quality due to the low presence of specialist birds

Multi-Label Bird Species Classification Using Sequential Aggregation Strategy from Audio Recordings

Birds are excellent bioindicators, playing a vital role in maintaining the delicate balance of ecosystems. Identifying species from bird vocalization is arduous but has high research gain. The paper focuses on the detection of multiple bird vocalizations from recordings. The proposed work uses a deep convolutional neural network (DCNN) and a recurrent neural network (RNN) architecture to learn the bird's vocalization from mel-spectrogram and mel-frequency cepstral coefficient (MFCC), respectively. We adopted a sequential aggregation strategy to make a decision on an audio file. We normalized the aggregated sigmoid probabilities and considered the nodes with the highest scores to be the target species. We evaluated the proposed methods on the Xeno-canto bird sound database, which comprises ten species. We compared the performance of our approach to that of transfer learning and Vanilla-DNN methods. Notably, the proposed DCNN and VGG-16 models achieved average F1 metrics of 0.75 and 0.65, respectively, outperforming the acoustic cue-based Vanilla-DNN approach

In Search for a Generalizable Method for Source Free Domain Adaptation

Source-free domain adaptation (SFDA) is compelling because it allows adapting an off-the-shelf model to a new domain using only unlabelled data. In this work, we apply existing SFDA techniques to a challenging set of naturally-occurring distribution shifts in bioacoustics, which are very different from the ones commonly studied in computer vision. We find existing methods perform differently relative to each other than observed in vision benchmarks, and sometimes perform worse than no adaptation at all. We propose a new simple method which outperforms the existing methods on our new shifts while exhibiting strong performance on a range of vision datasets. Our findings suggest that existing SFDA methods are not as generalizable as previously thought and that considering diverse modalities can be a useful avenue for designing more robust models

Évaluation du comptage des détections pour estimer la densité d'oiseaux à l'aide d'un suivi sonore passif : recommandations pour estimer un taux de détections fiable

Cue counting is a method developed for estimating vocally active wildlife density by dividing the density of cues (number of cues per unit area surveyed per unit time) by the average cue rate (ACR) at which individuals vocalize. It has been used successfully to estimate whale density using passive acoustic monitoring, but its efficacy has had limited testing in birds. We tested whether cue counting can be used to infer bird abundance using autonomous recording units and estimated the minimum effort required to obtain a reliable cue rate at individual and population levels. We recorded Dupont's Lark (Chersophilus duponti) vocalizations at 31 sites where traditional field censuses were also performed. We estimated the ACR using three methodologies: directional recordings, recordings from an online database of bird sounds (xeno-canto), and behavioral field studies. The ACRs estimated using directional recordings and behavioral field studies were similar, and bird numbers were over and underestimated by 0.8 and 10%, respectively (74–77% of the sampling sites were well estimated). However, the ACR estimated using xeno-canto recordings was much higher than those estimated using the other two methods, and bird numbers were underestimated by 41%. We also performed a cost-effectiveness assessment of the number of individuals and recording durations needed to optimize the estimation of a reliable ACR. We found that ACR estimates were more efficient if long (25 min) recordings were used when < 4 males were recorded, whereas 5-min recordings were more efficient for ≥ 20 males. We conclude that cue counting can be useful to infer bird density around recorders but requires an accurate measure of the ACR. Further research should evaluate the effectiveness of passive cue counting on a large number of species and under different circumstances.Le comptage des détections est une méthode qui a été élaborée pour estimer la densité de la faune active vocalement en divisant la densité de détections (nombre de détections par unité de surface étudiée par unité de temps) par le taux moyen de détections (TMD) auquel les individus chantent ou crient. Cette méthode a été utilisée avec succès pour calculer la densité des baleines à l'aide d'un suivi sonore passif, mais son efficacité a peu été testée chez les oiseaux. Nous avons testé si le comptage des détections pouvait être utilisé pour déduire l'abondance des oiseaux en utilisant des enregistreurs automatisés et avons calculé l'effort minimum requis pour obtenir un taux de détections fiable au niveau des individus et des populations. Nous avons enregistré les manifestations sonores du Sirli de Dupont (Chersophilus duponti) sur 31 sites où des recensements traditionnels ont également été effectués. Nous avons calculé le TMD de trois façons : à partir d'enregistrements directionnels, d'enregistrements provenant d'une base de données de cris et de chants d'oiseaux accessible en ligne (xeno-canto) et d'études comportementales sur le terrain. Les TMD calculés à l'aide d'enregistrements directionnels et d'études comportementales étaient similaires, et le nombre d'oiseaux était surestimé et sous-estimé de 0,8 et 10 %, respectivement (la mesure de 74-77 % des sites d'échantillonnage était juste). Cependant, le TMD calculé à l'aide d'enregistrements de xeno-canto était beaucoup plus élevé que ceux obtenus au moyen des deux autres méthodes, et le nombre d'oiseaux était sous-estimé de 41 %. Nous avons également réalisé une évaluation coût-efficacité du nombre d'individus et des durées d'enregistrement nécessaires pour optimiser le calcul d'un TMD fiable. Nous avons constaté que la mesure du TMD était meilleure si des enregistrements longs (25 min) étaient utilisés lorsque < 4 mâles étaient enregistrés, tandis que des enregistrements de 5 min étaient plus efficaces pour ≥ 20 mâles. Nous concluons que le comptage des détections peut être utile pour calculer la densité d'oiseaux autour des enregistreurs, mais une mesure précise du TMD doit d'abord être effectuée. D'autres recherches devraient se pencher sur l'évaluation de l'efficacité du comptage passif des détections dans le cas d'un grand nombre d'espèces et dans différentes circonstances.This study was supported by the Programa de Investigación y Conservación del Zoo de Barcelona within the project “Nuevas tecnologías para viejos trabajos: Uso de grabadores automáticos para la detección y censo de especies raras y amenazadas: El caso de la alondra ricotí en Lleida y otras poblaciones pequeñas”; the Education, Youth and Sport Bureau (Madrid Regional Government); and the European Social Fund for the Youth Employment Initiative (PEJ15/AMB/AI-0059 and PEJ1D-2018-PRE/AMB-8063). This work is a contribution to the Excellence Network Remedinal 3CM (S2013/MAE2719), supported by Comunidad de Madrid; and to the projects “Census of Dupont’s Lark in Guadalajara 2017 (SSCC/046/2017)”, supported by Junta de Comunidades de Castilla-La Mancha; LIFE Ricotí (LIFE15-NAT-ES-000802), supported by the European Commission, and “BBVA-Dron Ricotí”, funded by the BBVA Foundation

Correlation Clustering of Bird Sounds

Bird sound classification is the task of relating any sound recording to those species of bird that can be heard in the recording. Here, we study bird sound clustering, the task of deciding for any pair of sound recordings whether the same species of bird can be heard in both. We address this problem by first learning, from a training set, probabilities of pairs of recordings being related in this way, and then inferring a maximally probable partition of a test set by correlation clustering. We address the following questions: How accurate is this clustering, compared to a classification of the test set? How do the clusters thus inferred relate to the clusters obtained by classification? How accurate is this clustering when applied to recordings of bird species not heard during training? How effective is this clustering in separating, from bird sounds, environmental noise not heard during training?Comment: 13 page

Evaluating citizen science for dialect research on the nightingale song (Luscinia megarhynchos)

Citizen Science (CS) ist eine Methode, die in den letzten Jahren in der Wissenschaft weltweit an Bedeutung gewonnen hat. Obwohl viele Studien diese Daten mit denen von akademischen Forschenden verglichen, gibt es immer noch Bedenken hinsichtlich ihrer Qualität. In meiner Doktorarbeit zielte ich darauf ab die Methode CS für eine Vogelart mit einem großen Repertoire, der Nachtigall (Luscinia megarhynchos), als Anwendungsfall auf der Grundlage der Dialektforschung zu evaluieren. Ich untersuchte, ob die drei vermeintlichen Hauptgründe für schlechte Qualität (Anonymität, Unerfahrenheit und fehlende Standardisierung) zu unvollständigen, zeitlich oder räumlich verzerrten und ungenauen bioakustischen Daten führten. Dazu analysierte ich nicht-standardisierte CS-Aufnahmen, die mit einem Smartphone über die 'Naturblick' App erstellt wurden, welche einen eingebauten Mustererkennungsalgorithmus enthielt. Ich konnte in meiner Doktorarbeit zeigen, dass mit der Methode CS valide Daten für die bioakustische Forschung gewonnen werden können. Meine Ergebnisse zeigten, dass Anonymität, mangelnde Erfahrung und Standardisierung nicht zu geringer Qualität führten, sondern zu einem großen Datensatz, der genauso wertvoll war wie jene von akademischen Forschenden. Die Ergebnisse sind von großer Bedeutung für künftige CS-Projekte zur Verbesserung der Qualität und des Vertrauens in diese Daten.Citizen science (CS) is a method that has been increased in science worldwide in recent years. Although many studies have compared these data with those of academic researchers, there are still concerns about their quality. In my doctoral thesis I aimed to evaluate the method of CS for a bird species with a large repertoire, the nightingale (Luscinia megarhynchos), as a use case based on dialect research. I investigated whether the three main assumed reasons for poor quality (anonymity, inexperience and lack of standardisation) led to incomplete, temporal or spatial biassed and inaccurate bioacoustic data. Therefore, I analysed non-standardised CS recordings, which were generated with a smartphone via the 'Naturblick' app, which contained an in-built pattern recognition algorithm. In summary (Chapter V), my doctoral thesis showed that the method CS could be used to generate valid data for bioacoustic research. My findings showed that anonymity, lack of experience and standardisation did not lead to low quality but in fact to a large dataset, which was as valuable as ones from academic researchers. The results are of great relevance for future CS projects to improve the quality and the trust in these data