Acoustics, balance, and chimpanzees – The ABCs of developing a deep learning-based automated acoustic detector for wild chimpanzee (Pan troglodytes) loud calls

Acoustics, balance, and chimpanzees – The ABCs of developing a deep learning-based automated acoustic detector for wild chimpanzee (Pan troglodytes) loud calls | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 29 January 2025 V1 Latest version Share on Acoustics, balance, and chimpanzees – The ABCs of developing a deep learning-based automated acoustic detector for wild chimpanzee (Pan troglodytes) loud calls Authors : Adrienne Chitayat 0000-0003-2651-4010 , Jan Clemens , Catherine Crockford , Anne-Sophie Crunchant 0000-0002-4277-2055 , Ammie Kalan , Alex Piel , Fiona Stewart , and Serge Wich [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.173814850.06519206/v1 402 views 313 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract 1. Passive acoustic monitoring (PAM) is a powerful tool for wildlife monitoring, but the time and expertise required to process large volumes of data pose significant challenges. Automated acoustic detectors improve efficiency by speeding up data processing. Class imbalance, resulting from fewer target signals relative to noise, complicates development and can negatively impact performance. However, training datasets should also reflect the conditions of real-world PAM datasets. 2. We developed an automated acoustic detector for chimpanzee loud calls while addressing class imbalance. We predicted that greater data diversity and high-quality data (clear signals, minimal noise interference) would enhance network performance and that class imbalance, by supporting diversity, is essential for functionality. We built training datasets with data recorded in wild settings and applied a temporal convolutional neural network approach using Deep Audio Segmenter (DAS). We trained networks using datasets containing varying levels of noise (50%, 75%, 90%, 99%) and also tested the effectiveness of frequency removal in improving performance. 3. The network performances varied significantly, with F1 scores of 0.44 to 0.86, exceeding a previous study (5% F1). The most imbalanced dataset produced the best performing network, capturing 90% of pant-hoot events and annotating them with 90% (SD = 20.9) accuracy. The results showed that increased class size was associated with greater intraclass diversity and improved precision (0.41–0.83). The networks showed consistently high recall rates, especially when frequency removal was not applied (0.89–0.92). 4. This study stresses the importance of class size and diversity in developing automated acoustic detectors. It also highlights the value of high-quality data for accurate pattern recognition of the target signal and the importance of the noise class for effective class decoupling and detector functionality. This research supports the advancement of PAM in chimpanzee studies, opening new opportunities to integrate remote sensing for efficient wildlife monitoring. Supplementary Material File (chitayat et al_ee_main document.pdf) Download 958.09 KB Information & Authors Information Version history V1 Version 1 29 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords behavioral ecology method development terrestrial vertebrate Authors Affiliations Adrienne Chitayat 0000-0003-2651-4010 University of Amsterdam Faculty of Science View all articles by this author Jan Clemens European Neuroscience Institute Göttingen View all articles by this author Catherine Crockford Marc Jeannerod Institute for Cognitive Sciences View all articles by this author Anne-Sophie Crunchant 0000-0002-4277-2055 Wild Chimpanzee Foundation View all articles by this author Ammie Kalan University of Victoria Faculty of Science View all articles by this author Alex Piel University College London View all articles by this author Fiona Stewart University College London View all articles by this author Serge Wich [email protected] Liverpool John Moores University View all articles by this author Metrics & Citations Metrics Article Usage 402 views 313 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Adrienne Chitayat, Jan Clemens, Catherine Crockford, et al. Acoustics, balance, and chimpanzees – The ABCs of developing a deep learning-based automated acoustic detector for wild chimpanzee (Pan troglodytes) loud calls. Authorea . 29 January 2025. DOI: https://doi.org/10.22541/au.173814850.06519206/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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