Abstract
This article focuses on the procedure to automatically identify Alytes obstetricans vocalisations, an anuran species that emits calls when mating. In Luxembourg, 37 sites where the species was historically or recently recorded were monitored using automated sound recording systems (ARS) during spring and summer 2021. The huge amount of audio recordings collected were processed using scikit-maad, an open-source Python package dedicated to the quantitative analysis of environmental audio recordings. Our results show that the SVC method at high resolution presents the best results to predict A. obstetricans calls. With the help of the MAAD package, we were able to build several models that detect A. obstetricans calls with high efficiency, which seems to be a promissing alternative method to monitor the common midwife toad in Luxembourg.
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Acknowledgement
This study was performed in the frame of the ACOUWIFE project funded by the Ministry of the Environment, Climate and Sustainable Development, through the environmental protection fund. We warmly thank A. Dohet, X. Mestdagh and Y. Martin for their contribution in the setting up of the ARS and the data collection in the field.
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Didry, Y., L’Hoste, L., Vray, S. (2022). Automatic Identification of “Alytes obstetricans” Calls. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2022. Lecture Notes in Computer Science, vol 13492. Springer, Cham. https://doi.org/10.1007/978-3-031-16538-2_28
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DOI: https://doi.org/10.1007/978-3-031-16538-2_28
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