BirdNET: applications, performance, pitfalls and future opportunities

Ibis, Год журнала: 2023, Номер 165(3), С. 1068 - 1075

Опубликована: Фев. 27, 2023

Automated recognition software is paramount for effective passive acoustic monitoring. BirdNET a free and recently developed bird sound recognizer. I performed literature review to evaluate the current applications performance of BirdNET, which growing in popularity but has been subject few assessments, provide recommendations future studies using BirdNET. Prior research employed wide range purposes have linked detections ecological processes or real‐world monitoring schemes. Among evaluated studies, average precision (% correctly identified) usually ranged around 72–85%, recall rate target species vocalizations detected) 33–84%. Some did not assess performance, hampers interpretation results may poorly informed decisions. Recommendations on how efficiency are provided. The impact confidence score threshold, user‐selected parameter as minimum reported, output although variable among consistent. use high thresholds increases percentage classified lowers proportion calls detected. selection an optimal depend priorities user goals. great tool automated it should be used with caution due inherent challenges identification. continued refinement suggests further improvements coming years.

Язык: Английский

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Hearing to the Unseen: AudioMoth and BirdNET as a Cheap and Easy Method for Monitoring Cryptic Bird Species

Sensors, Год журнала: 2023, Номер 23(16), С. 7176 - 7176

Опубликована: Авг. 15, 2023

The efficient analyses of sound recordings obtained through passive acoustic monitoring (PAM) might be challenging owing to the vast amount data collected using such technique. development species-specific recognizers (e.g., deep learning) may alleviate time required for but are often difficult create. Here, we evaluate effectiveness BirdNET, a new machine learning tool freely available automated recognition and processing, correctly identifying detecting two cryptic forest bird species. BirdNET precision was high both Coal Tit (Peripatus ater) Short-toed Treecreeper (Certhia brachydactyla), with mean values 92.6% 87.8%, respectively. Using default values, successfully detected in 90.5% 98.4% annotated recordings, We also tested impact variable confidence scores on performance estimated optimal score each Vocal activity patterns species, PAM reached their peak during first hours after sunrise. hope that our study encourage researchers managers utilize this user-friendly ready-to-use software, thus contributing advancements sensing environmental monitoring.

Язык: Английский

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A First Assessment of Birdnet Performance at Varying Distances: A Playback Experiment

Ardeola, Год журнала: 2023, Номер 70(2)

Опубликована: Май 15, 2023

Las vocalizaciones de las aves, como cualquier otra señal acústica, se atenúan con la distancia y, por lo tanto, estructura aves degrada progresivamente. Tal degradación puede tener un impacto en capacidad programas automatizados reconocimiento señales a hora detectar e identificar correctamente aves. BirdNET es reconocedor automatizado cantos pájaros reciente creación y comúnmente empleado investigadores el público. Sin embargo, pocos estudios han evaluado su rendimiento nuestro conocimiento actual sobre cómo variar función o entre especies muy limitado. Aquí, mi objetivo era evaluar si habilidad para tres variaba según distancia, tipo grabadora empleada especies, utilizando una grabación reproducida 10 150 m. La los varió general, disminuyó pero no dos tipos grabadores testados. tasa detección BirdNET, definida porcentaje detectadas identificadas software, fue del 59,9% (499 840 reproducidas). Se identificó manera correcta significativa mayor número cuando emitieron 50 m más cerca (tasa media 92,2%), comparación emitidas esa 34,9%). también significativamente alta chingolo saltamontes reinita encapuchada, vireo gris. El clasificaciones erróneas distancias siguió patrón lineal. Ese estudio proporciona información valiosa que contribuir mejorar futuros muestreos expandir uso censar comunidades usando monitoreo acústico pasivo.—Pérez-Granados, C. (2023). Un primer análisis variables: experimento playback. Ardeola, 70: 221-233.

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Passive acoustic monitoring and automated detection of the American bullfrog

Biological Invasions, Год журнала: 2024, Номер 26(4), С. 1269 - 1279

Опубликована: Янв. 25, 2024

Abstract Biological invasions pose significant threats to biodiversity and ecosystem functioning. Removal of introduced species is most successful when detected early. We evaluate the effectiveness passive acoustics combined with automated recognition in detecting invasive American bullfrog ( Lithobates catesbeianus ). applied this technique two real-world monitoring programs aimed at determining optimal time day for Europe, which we recorded Belgium Italy; evaluating BirdNET (a free user-friendly recognizer) analyzing a large dataset collected Spain. was highly effective automatically presence, detection rate (compared visual inspection sonograms) 89.5% using default settings (85 95 recordings known presence), 95.8% user-specific (91 detected). The system showed remarkable precision, correctly identifying 99.7% (612 out 614) verified predictions, only one mislabelled recording (predicted be present it absent). species’ vocal activity Italy higher during night compared crepuscular periods. Recording analyses output verification Spain carried 3.8% time, resulted significantly reduced effort inspection. Our study highlights remotely surveying bullfrog, making potential tool informing management decisions, particularly early arrival new areas.

Язык: Английский

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The use of BirdNET embeddings as a fast solution to find novel sound classes in audio recordings

Frontiers in Ecology and Evolution, Год журнала: 2025, Номер 12

Опубликована: Янв. 16, 2025

Passive acoustic monitoring has emerged as a useful technique for vocal species and contributing to biodiversity goals. However, finding target sounds without pre-existing recognisers still proves challenging. Here, we demonstrate how the embeddings from large model BirdNET can be used quickly easily find new sound classes outside original model’s training set. We outline general workflow, present three case studies covering range of ecological use cases that believe are common requirements in research management: invasive species, generating lists, detecting threatened species. In all cases, minimal amount class examples validation effort was required obtain results applicable desired application. The demonstrated success this method across different datasets taxonomic groups suggests wide applicability novel classes. anticipate will allow easy rapid detection which no current exist, both conservation

Язык: Английский

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The potential for AI to revolutionize conservation: a horizon scan

Trends in Ecology & Evolution, Год журнала: 2024, Номер unknown

Опубликована: Дек. 1, 2024

Язык: Английский

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A Quantitative Evaluation of the Performance of the Low-Cost AudioMoth Acoustic Recording Unit

Sensors, Год журнала: 2023, Номер 23(11), С. 5254 - 5254

Опубликована: Июнь 1, 2023

The AudioMoth is a popular autonomous recording unit (ARU) that widely used to record vocalizing species in the field. Despite its growing use, there have been few quantitative tests on performance of this recorder. Such information needed design effective field surveys and appropriately analyze recordings made by device. Here, we report results two designed evaluate characteristics First, performed indoor outdoor pink noise playback experiments how different device settings, orientations, mounting conditions, housing options affect frequency response patterns. We found little variation acoustic between devices relatively effect placing recorders plastic bag for weather protection. has mostly flat on-axis with boost above 3 kHz, generally omnidirectional suffers from attenuation behind recorder, an accentuated when it mounted tree. Second, battery life under variety frequencies, gain environmental temperatures, types. standard alkaline batteries last average 189 h at room temperature using 32 kHz sample rate, lithium can twice as long freezing temperatures compared batteries. This will aid researchers both collecting analyzing generated

Язык: Английский

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Validation of the F-POD—A fully automated cetacean monitoring system

PLoS ONE, Год журнала: 2023, Номер 18(11), С. e0293402 - e0293402

Опубликована: Ноя. 17, 2023

The F-POD, an echolocation-click logging device, is commonly used for passive acoustic monitoring of cetaceans. This paper presents the first assessment error-rate fully automated analysis by this system, a description F-POD hardware, and KERNO-F v1.0 classifier which identifies click trains. Since 2020, twenty loggers have been in BlackCeTrends project research teams from Bulgaria, Georgia, Romania, Türkiye, Ukraine with aim investigating trends relative abundance populations cetaceans Black Sea. Acoustic data analysed here comprises 9 billion raw clicks total, 297 million were classified as Narrow Band High Frequency (NBHF) (harbour porpoise clicks) 91 dolphin clicks. Such volumes require reliable system analysis, we describe. A total 16,805 Detection Positive Minutes (DPM) individually inspected assessed visual check train characteristics each DPM. To assess overall error rate species group investigated 2,000 DPM having NBHF fraction containing misclassified trains was less than 0.1% dolphins corresponding 0.97%. For both groups porpoises dolphins), these error-rates are acceptable further study Sea using classification without editing data. main sources errors 0.17% boat sonar DPMs harbour porpoises, 0.14% dolphins. potential to estimate at generate makes possible new predictive approach estimation.

Язык: Английский

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Living Together, Singing Together: Revealing Similar Patterns of Vocal Activity in Two Tropical Songbirds Applying BirdNET

Sensors, Год журнала: 2024, Номер 24(17), С. 5780 - 5780

Опубликована: Сен. 5, 2024

In recent years, several automated and noninvasive methods for wildlife monitoring, such as passive acoustic monitoring (PAM), have emerged. PAM consists of the use sensors followed by sound interpretation to obtain ecological information about certain species. One challenge associated with is generation a significant amount data, which often requires machine learning tools recognition. Here, we couple BirdNET, free-to-use algorithm assess, first time, precision BirdNET in predicting three tropical songbirds describe their patterns vocal activity over year Brazilian Pantanal. The method was high all species (ranging from 72 84%). We were able two species, Buff-breasted Wren (Cantorchilus leucotis) Thrush-like (Campylorhynchus turdinus). Both presented very similar during day, maximum around sunrise, throughout year, peak occurring between April June, when food availability insectivorous may be high. Further research should improve our knowledge regarding ability coupling wider range

Язык: Английский

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Species Distribution Model Performance Improves When Habitat Characterizations are Centered on Detected Individuals Instead of Observers

Опубликована: Янв. 1, 2025

Species distribution models (SDMs) link species occurrence to environmental characteristics predict suitable habitats beyond known occurrences. The conventional procedure fit SDMs for individual organisms detected at some distance away from observers is characterize species' associated habitat based on observer's survey location. However, each surveyed may be in distinct those where are located. Here, we compared variables centered the observer and bird locations consequent effects SDM performance. We utilized remote sensing data observer- bird-locations three radii (pixel radius: 30-m; fixed 100-m; species-specific effective radius). trained Poisson boosted regression tree 111 species, leveraging structured professional surveys, eBird, tribal datasets. evaluated models' predictability with model performance metrics – deviance, Kendall's rank correlation coefficient, root mean square error. Models had higher coefficients than locations, yielding more reliable prediction maps. Using fixed-radius approach generally performed better pixel radii. of specialists generalists when characterization was instead surveyor locations. A percentage showed bird-location observer-location models. Our findings emphasize importance prioritizing characterizations individuals' enhance improve predictions.

Язык: Английский

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