BirdNET: applications, performance, pitfalls and future opportunities

Automated recognition software is paramount for effective passive acoustic monitoring. BirdNET is a free and recently developed bird sound recognizer. I performed a literature review to evaluate the current applications and performance of BirdNET, which is growing in popularity but has been subject...

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Bibliographic Details
Published in:Ibis (London, England) England), 2023-07, Vol.165 (3), p.1068-1075
Main Author: Pérez‐Granados, Cristian
Format: Article
Language:English
Subjects:
Acoustic tracking
bird sound recognition
Birds
confidence score
convolutional neural network
Ecological effects
Literature reviews
Monitoring
passive acoustic monitoring
precision
recall rate
Target detection
Vocalization behavior
Voice recognition
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Summary:Automated recognition software is paramount for effective passive acoustic monitoring. BirdNET is a free and recently developed bird sound recognizer. I performed a literature review to evaluate the current applications and performance of BirdNET, which is growing in popularity but has been subject to few assessments, and to provide recommendations for future studies using BirdNET. Prior research has employed BirdNET for a wide range of purposes but few studies have linked BirdNET detections to ecological processes or real‐world monitoring schemes. Among evaluated studies, average precision (% detections correctly identified) usually ranged around 72–85%, and recall rate (% target species vocalizations detected) ranged around 33–84%. Some studies did not assess BirdNET performance, which hampers the interpretation of the ecological results and may provide poorly informed decisions. Recommendations on how to evaluate BirdNET efficiency are provided. The impact of the confidence score threshold, a user‐selected parameter as the minimum score for detections reported, on BirdNET output although variable among species is consistent. The use of high confidence score thresholds increases the percentage of detections correctly classified but lowers the proportion of calls and bird species detected. The selection of an optimal score may depend on the priorities of the user and research goals. BirdNET is a great tool for automated bird monitoring but it should be used with caution due to inherent challenges for automated bird identification. The continued refinement of BirdNET suggests further improvements in the coming years.
ISSN:0019-1019
1474-919X
DOI:10.1111/ibi.13193