Enhancing an Avian Sound Recognition Model's Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus Rubecula)

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Shackleton, B, Passos, L orcid iconORCID: 0000-0003-4529-9950 and MacLeod, R orcid iconORCID: 0000-0001-5508-0202 (2026) Enhancing an Avian Sound Recognition Model's Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus Rubecula). Journal of Visualized Experiments (230). ISSN 1940-087X

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Abstract

Passive acoustic monitoring (PAM) has become an invaluable tool for biodiversity research, enabling the non-invasive collection of vast datasets. However, a significant challenge remains in efficiently and reliably processing this large volume of data to extract species-specific information across varying locations. This paper presents a detailed, step-by-step protocol to address this challenge using a machine learning detector module within a bioacoustics analysis software. The methodology is designed to accurately and confidently identify and validate bird vocalisations from raw acoustic recordings.

Our protocol details the process from initial data collection using autonomous recording units (ARUs) to the final generation of a high-quality annotated dataset. Key steps include configuring the machine learning detector module to generate initial detections, a manual validation procedure to calculate precision tables, and a logistic regression analysis to determine a species-specific and, where appropriate, a location-specific confidence score threshold. This statistically derived threshold is then used to refine the detector’s output, tested on two overlap configurations (0 s and 2 s). We show that applying the derived optimal confidence score thresholds substantially improves the machine learning-based avian sound recognition models detection precision across sites. For the three sites used to illustrate the process (Liverpool Park, Cairngorms, and Glasgow Suburban) precision increased by 26.1%, 17.7%, and 17% for an overlap of 0 s, and by 28.77%, 16.87%, and 15% for an overlap of 2 s. We suggest the resulting methodology is superior to manual counting methods in both speed and reliability. In summary, this paper provides a reproducible framework that facilitates the accessible and effective use of machine learning approaches in bioacoustics, enabling researchers to confidently leverage large acoustic datasets for ecological studies and parameter analysis.

Item Type: Article
Uncontrolled Keywords: 0601 Biochemistry and Cell Biology; 1701 Psychology; 1702 Cognitive Sciences; 3101 Biochemistry and cell biology
Subjects: Q Science > QA Mathematics > QA76 Computer software
Q Science > QL Zoology
Divisions: Biological and Environmental Sciences (from Sep 19)
Publisher: MyJove Corporation
Date of acceptance: 13 March 2026
Date of first compliant Open Access: 20 April 2026
Date Deposited: 20 Apr 2026 12:31
Last Modified: 20 Apr 2026 12:31
DOI or ID number: 10.3791/70479
URI: https://researchonline.ljmu.ac.uk/id/eprint/28381
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