Machine Learning Reveals Microdialect Variations in Salish Sea Melospiza melodia

Machine Learning Reveals Microdialect Variations in Salish Sea Melospiza melodia | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 2 January 2026 V1 Latest version Share on Machine Learning Reveals Microdialect Variations in Salish Sea Melospiza melodia Author : Vidhur Prabhu 0009-0006-9006-2476 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176733923.38290332/v1 314 views 170 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Understanding microdialects, or distinct variations in songbird (oscine) vocalizations, is critical to identifying populations that may act as environmental indicators for anthropogenic impact. Although vocal variation in avian species has been studied for decades, microdialect classification can be improved using the emerging fields of passive acoustic monitoring (PAM) and machine learning. This study aims to quantify the ability for neural networks to detect fine-scale geographical variations among contiguous populations through predicting an individual’s location. A lightweight ~0.92M parameter Convolutional Neural Network (CNN) was designed to identify the presence of microdialects in five populations of Salish Sea Melospiza melodia (song sparrow) from the Puget Sound and British Columbia using citizen science recordings (364 hr.) and date metadata. This was achieved with two complementary models: one to predict geographical coordinates through regression and another to classify recordings into discrete regions. The regression model achieved an average error of 35.16 km, a 60.95% improvement over the baseline average obtained by chance. The classification model performed with an accuracy of 63.02%, an increase of 43.02 percentage points over the baseline accuracy. These results demonstrate the capability of deep learning models to detect subtle variations among contiguous populations, allowing feasible implementation in terrestrial autonomous recording units (ARUs) with limited computational resources and variable field conditions. This suggests that deep learning can enable real-time, efficient monitoring of oscine populations in ecological studies. Supplementary Material File (regional_dialects_paper.pdf) Download 269.67 KB Information & Authors Information Version history V1 Version 1 02 January 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords behavioral ecology statistical terrestrial theoretical theory vertebrate Authors Affiliations Vidhur Prabhu 0009-0006-9006-2476 [email protected] Unaffiliated View all articles by this author Metrics & Citations Metrics Article Usage 314 views 170 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Vidhur Prabhu. Machine Learning Reveals Microdialect Variations in Salish Sea Melospiza melodia. Authorea . 02 January 2026. DOI: https://doi.org/10.22541/au.176733923.38290332/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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