Cristian Pérez-Granados

Yizhar Lavner

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Frontiers in Bird ScienceOPEN ACCESSEDITED AND REVIEWED BYDes B. A. Thompson,Edinburgh, United Kingdom*CORRESPONDENCEYizhar Lavneryizharle@telhai.ac.ilCristian Pérez-Granadoscristian.perez@ua.esRECEIVED 27 October 2024ACCEPTED 06 December 2024PUBLISHED 23 December 2024CITATIONLavner Y and Pérez-Granados C (2024)Editorial: Computational bioacoustics andautomated recognition of bird vocalizations:new tools, applications and methodsfor bird monitoring.Front. Bird Sci. 3:1518077.doi: 10.3389/fbirs.2024.1518077COPYRIGHT© 2024 Lavner and Pérez-Granados. This is anopen-access article distributed under the termsof the Creative Commons Attribution License(CC BY). The use, distribution or reproductionin other forums is permitted, provided theoriginal author(s) and the copyright owner(s)are credited and that the original publicationin this journal is cited, in accordance withaccepted academic practice. No use,distribution or reproduction is permittedwhich does not comply with these terms.TYPE EditorialPUBLISHED 23 December 2024DOI 10.3389/fbirs.2024.1518077Frontiers in Bird ScienceEditorial: Computationalbioacoustics and automatedrecognition of bird vocalizations:new tools, applications andmethods for bird monitoringYizhar Lavner1* and Cristian Pérez-Granados2,3*1Department of Computer Science, Tel-Hai College, Upper Galilee, Israel, 2Ecology Department,Alicante University, Alicante, Spain, 3Conservation Biology Group, Landscape Dynamics andBiodiversity Program, Forest Science and Technology Center of Catalonia (CTFC), Lleida, SpainKEYWORDSmachine learning, deep learning, passive acoustic monitoring, bioacoustics, long-termbird monitoring, biodiversity, bird vocalizations, audio signal processingEditorial on the Research TopicComputational bioacoustics and automated recognition of birdvocalizations: new tools, applications and methods for bird monitoringIn recent decades, technological advancements have significantly transformed wildlifemonitoring methods, particularly through the adoption of automated and non-invasivetechniques like passive acoustic monitoring (PAM). PAM involves deploying soundrecorders in the field to capture audio during specified times, which are followed bysound interpretation to gather information on the presence and behavior of species. Amajor challenge associated with PAM is the vast amount of audio data generated,necessitating the development of automated processing techniques utilizing machinelearning, deep learning, or other advanced audio signal processing methods (Stowell,2022). Birds have been the primary focus of PAM studies, making our understanding ofhow to effectively monitor and automatically identify bird species from audio recordingswell-established. Indeed, PAM has been shown to be a reliable method for estimating birdspecies richness and population densities from sound recordings (Darras et al., 2019; Pérez-Granados and Traba, 2021). Additionally, it has been employed to investigate variousecological questions, including the detection of previously unnoticed habitat changes andshifts in biodiversity patterns (Ross et al., 2023).However, despite these advances, there remain many challenges and uncertainties thatneed to be addressed, such as improving automated species identification accuracy, modelevaluation, or pairing PAM with other automated techniques. Through this ResearchTopic, we present recent advancements in tools, methods, and algorithms for automatedbird identification to enhance bird monitoring programs utilizing PAM.One of the most important tasks in computational bioacoustics is tracking andmonitoring biodiversity changes, which have been greatly advanced using PAM. Amongthe factors influencing biodiversity, agriculture plays a major role. Employing PAM tofrontiersin.org01Lavner and Pérez-Granados 10.3389/fbirs.2024.1518077assess the impact of agricultural practices on species occupancy anddiversity requires careful planning and consideration of key factorsfor successful research. The work of Molina-Mora et al. discussesthese factors and use PAM for assessing the impact of agriculturalmanagement practices on biodiversity, using birds as indicators.Specifically, their research focuses on coffee-growing areas in CostaRica, comparing the effects of pruning and pesticide use over twoyears. Utilizing a mobile application for community-drivenbioacoustic annotations to identify the vocalizations of selectedspecies, they report that pruning negatively affects some species,while pesticide application reduces vocal activity and presumablythe presence of all species studied.Currently, most of PAM projects use automated signalrecognition software and machine learning models to detect theirspecies of interest (Kahl et al., 2021; Cole et al., 2022; Pérez-Granados, 2023; Lavner et al., 2024; Sethi et al., 2024). Yet, theapplication of machine learning models faces significant challengesdue to the unique characteristics of bioacoustic data and thecomplexity of real-world deployment. For instance, PAMrecordings often have low SNR, as bird vocalizations areembedded in substantial background noise. There is also amismatch between training data, typically made with directionalmicrophones, and field-recorded PAM data, which capturesmultiple species simultaneously, often with background noise.Additional challenges include class imbalance, distribution shifts,and inconsistent annotation quality, all of which hinder machinelearning models’ ability to generalize across varied conditions.Two approaches are presented in the Research Topic to addressthese challenges: specialized models trained on specific datasets andtailored to answer particular tasks or specific research questions,and generalizable models capable of adapting to a variety of datasetsand tasks.A specialized solution is offered by Haley et al. who track theactivity of mountain chickadee (Poecile gambeli) in North America,comparing a custom CNN model (Madhusudhana et al., 2021) toBirdNET (Khal et al., 2021). Their custom model, trainedexclusively on PAM recordings, exhibited better performance andgreater resilience to noise than BirdNET model, which highlightsthat specific models trained on specialized (i.e. locally trained)datasets may offer an advantage by being more closely alignedwith the audio recording data.In construct, van Merriënboer et al. advocate for theconstruction of bioacoustic generalizable or foundation models(“large scale machine learning models that are trained on broaddata that can be adapted to a wide variety of downstream tasks”,Bommasani et al., 2021), designed to learn from a specific trainingset or even a small number of examples, [few shot learning (Nolascoet al., 2023; Ghani et al., 2023)] generalize to new species,environments, and tasks beyond those they were initially trainedon. They provide a comprehensive review of the challengesassociated with developing such models, as well as the methodsand metrics used for their evaluation.Another challenge in species classification for PAM is thecommon practice of setting a threshold to determine whether aFrontiers in Bird Science 02species’ vocalization is present in an audio clip. Typically,classification scores above the threshold are counted as detections(Pérez-Granados, 2023). Such selection affects the number of falsepositives or negatives, which can vary across different datasets orspecies, leading to suboptimal classifications. To resolve this issue,Navine et al. developed a ‘threshold-free’ bioacoustics analysisframework, which directly estimates call density—the proportionof detection windows containing the target vocalizations—regardless of classifier score, which may be applied to binarydetection classifiers with fixed-size windows. A validation methodestimates call density in a dataset, and probability distributions aregenerated for confidence scores in both positive and negativeclasses. These distributions are used to predict call densities at thesite level, accounting for potential shifts in data distribution. Testingon real-world recordings of Hawaiian birds demonstrates thisapproach’s robustness to variations in call density andclassifier performance.In PAM surveys it is difficult to estimate the precise location orthe identity of vocalizing individuals, though such information isuseful for studying birds’ behavior or community dynamics.Guggenberger et al. propose a solution to this problem bycoupling a video camera to an acoustic recorder equipped with a64 microphone array, that enables to localize dozens of individuals,with high temporal and spatial resolution. To demonstrate itspotential, they recorded and localized Arabian babblers (Argyasquamiceps) during snake-mobbing events. The tagged birdsallowed for precise identification of callers and their vocal timing,enabling the reconstruction of vocal social networks. The analysisrevealed a periodic pattern in vocalizations, with age-specific inter-call duration, suggesting that snake-mobbing behavior is a learnedactivity within the group.This topic highlights the potential of PAM for bird monitoring,including studying shifts in bird communities due to human landuse, analyzing birds’ social networks, and improving thedevelopment and assessment of robust, generalizable machinelearning models. A significant challenge in advancing automatedbird detection models is the scarcity of annotated PAM datasets thatspan diverse habitats and geographic regions worldwide. To addressthis, the bioacoustic research community should collaborate tocreate a global, standardized, and annotated PAM dataset. Bypooling resources and adhering to common protocols, researcherscan promote the development of new, comparable methods forpassive bird surveys through the construction and use of scalablefoundation models for bioacoustic monitoring, similar to successfulapplications in other fields (O’Neill et al., 2023). Such efforts wouldnot only advance species identification and acoustic monitoring butalso contribute significantly to global nature conservation.Author contributionsYL: Conceptualization, Writing – original draft, Writing –review & editing. CP: Conceptualization, Writing – original draft,Writing – review & editing.frontiersin.orgLavner and Pérez-Granados 10.3389/fbirs.2024.1518077Conflict of interestThe authors declare that the research was conducted in theabsence of any commercial or financial relationships that could beconstrued as a potential conflict of interest.The author(s) declared that they were an editorial boardmember of Frontiers, at the time of submission. This had noimpact on the peer review process and the final decision.Frontiers in Bird Science 03Publisher’s noteAll claims expressed in this article are solely those of the authorsand do not necessarily represent those of their affiliatedorganizations, or those of the publisher, the editors and thereviewers. Any product that may be evaluated in this article, orclaim that may be made by its manufacturer, is not guaranteed orendorsed by the publisher.ReferencesBommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., et al.(2021). On the opportunities and risks of foundation models. arXiv preprintarXiv:2108.07258. doi: 10.48550/arXiv.2108.07258Cole, J. S., Michel, N. L., Emerson, S. A., and Siegel, R. B. (2022). Automated bird soundclassifications of long-duration recordings produce occupancy model outputs similar tomanually annotated data. Ornithol. Appl. 124, duac003. doi: 10.1093/ornithapp/duac003Darras, K., Batáry, P., Furnas, B. J., Grass, I., Mulyani, Y. A., and Tscharntke, T.(2019). Autonomous sound recording outperforms human observation for samplingbirds: a systematic map and user guide. Ecol. Appl. 29, e01954. doi: 10.1002/eap.v29.6Ghani, B., Denton, T., Kahl, S., and Klink, H. (2023). Global birdsong embeddingsenable superior transfer learning for bioacoustic classification. Sci. Rep. 13, 22876.doi: 10.1038/s41598-023-49989-zKahl, S., Wood, C. M., Eibl, M., and Klinck, H. (2021). BirdNET: A deep learning solutionfor avian diversity monitoring. Ecol. Inform. 61, 101236. doi: 10.1016/j.ecoinf.2021.101236Lavner, Y., Melamed, R., Bashan, M., and Vortman, Y. (2024). The bioacousticsoundscape of a pandemic: continuous annual monitoring using a deep learning systemin Agmon Hula Lake Park. Ecol. Inf. 80, 2024. doi: 10.1016/j.ecoinf.2024.102528Madhusudhana, S., Shiu, Y., Klinck, H., Fleishman, E., Liu, X., Nosal, E. M., et al. (2021).Improve automatic detection of animal call sequences with temporal context. J. R. SocInterface 18, 20210297. doi: 10.1098/rsif.2021.0297Nolasco, I., Singh, S., Morfi, V., Lostanlen, V., Strandburg-Peshkin, A., Vidaña-Vila,E., et al. (2023). Learning to detect an animal sound from five examples. Ecol. Inf. 77,102258. doi: 10.1016/j.ecoinf.2023.102258O’Neill, A., Rehman, A., Gupta, A., Maddukuri, A., Gupta, A., Padalkar, A., et al.(2023). Open x-embodiment: Robotic learning datasets and rt-x models. arXiv preprintarXiv:2310.08864. doi: 10.48550/arXiv.2310.08864Pérez-Granados, C. (2023). BirdNET: applications, performance, pitfalls and futureopportunities. Ibis 165, 1068–1075. doi: 10.1111/ibi.v165.3Pérez-Granados, C., and Traba, J. (2021). Estimating bird density using passiveacoustic monitoring: a review of methods and suggestions for further research. Ibis 163,765–783. doi: 10.1111/ibi.v163.3Ross, S. R. J., O’Connell, D. P., Deichmann, J. L., Desjonquères, C., Gasc, A., Phillips,J. N., et al. (2023). Passive acoustic monitoring provides a fresh perspective onfundamental ecological questions. Funct. Ecol. 37, 959–975. doi: 10.1111/1365-2435.14275Sethi, S. S., Bick, A., Chen, M. Y., Crouzeilles, R., Hillier, B. V., Lawson, J., et al.(2024). Large-scale avian vocalization detection delivers reliable global biodiversityinsights. Proc. Natl. Acad. Sci. 121, e2315933121. doi: 10.1073/pnas.2315933121Stowell, D. (2022). Computational bioacoustics with deep learning: a review androadmap. PeerJ 10, e13152. doi: 10.7717/peerj.13152frontiersin.org

Editorial: Computational bioacoustics and automated recognition of bird vocalizations: new tools, applications and methods for bird monitoring

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Editorial: Computational bioacoustics and automated recognition of bird vocalizations: new tools, applications and methods for bird monitoring

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