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

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Title: Hearing to the Unseen: AudioMoth and BirdNET as a Cheap and Easy Method for Monitoring Cryptic Bird Species
Authors: Bota, Gerard | Manzano-Rubio, Robert | Catalán, Lidia | Gómez-Catasús, Julia | Pérez-Granados, Cristian
Research Group/s: Ecología y Conservación de Poblaciones y Comunidades Animales (ECPCA)
Center, Department or Service: Universidad de Alicante. Departamento de Ecología
Keywords: Acoustic sensor | Audio recognition | Automated recognition software | Autonomous recording unit | Machine learning | Paridae | Periparus ater | Passive acoustic monitoring | Wildlife monitoring
Issue Date: 15-Aug-2023
Publisher: MDPI
Citation: Bota G, Manzano-Rubio R, Catalán L, Gómez-Catasús J, Pérez-Granados C. Hearing to the Unseen: AudioMoth and BirdNET as a Cheap and Easy Method for Monitoring Cryptic Bird Species. Sensors. 2023; 23(16):7176. https://doi.org/10.3390/s23167176
Abstract: The efficient analyses of sound recordings obtained through passive acoustic monitoring (PAM) might be challenging owing to the vast amount of data collected using such technique. The development of species-specific acoustic recognizers (e.g., through deep learning) may alleviate the time required for sound recordings but are often difficult to create. Here, we evaluate the effectiveness of BirdNET, a new machine learning tool freely available for automated recognition and acoustic data processing, for correctly identifying and detecting two cryptic forest bird species. BirdNET precision was high for both the Coal Tit (Peripatus ater) and the Short-toed Treecreeper (Certhia brachydactyla), with mean values of 92.6% and 87.8%, respectively. Using the default values, BirdNET successfully detected the Coal Tit and the Short-toed Treecreeper in 90.5% and 98.4% of the annotated recordings, respectively. We also tested the impact of variable confidence scores on BirdNET performance and estimated the optimal confidence score for each species. Vocal activity patterns of both species, obtained using PAM and BirdNET, reached their peak during the first two hours after sunrise. We hope that our study may encourage researchers and managers to utilize this user-friendly and ready-to-use software, thus contributing to advancements in acoustic sensing and environmental monitoring.
Sponsor: This research was partially funded by Red Eléctrica Española with the support of the Navarra Regional Government. CPG acknowledges the support of the Ministerio of Educación y Formación Profesional through the Beatriz Galindo Fellowship (Beatriz Galindo—Convocatoria 2020). JGC is funded by a Margarita Salas postdoctoral fellowship (CA4/RSUE/2022-00205) funded by the Spanish Ministry of Universities, the Recovery, Transformation, and Resilience Plan (key instrument for the development of Next Generation EU recovery funds) and Universidad Autónoma de Madrid.
URI: http://hdl.handle.net/10045/136813
ISSN: 1424-8220
DOI: 10.3390/s23167176
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Peer Review: si
Publisher version: https://doi.org/10.3390/s23167176
Appears in Collections:INV - ECPCA - Artículos de Revistas

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