AUTOMATED ECOACOUSTIC MONITORING: EVALUATING CLASSIFIER PERFORMANCE FOR COMMON CUCKOO (CUCULUS CANORUS) DETECTION

Whitehead, Helen

Abstract

This study evaluates the effectiveness of automated bioacoustic classifiers in detecting the vocal activity of the common cuckoo (Cuculus canorus) within the Chornobyl Exclusion Zone (CEZ). Data were collected from 12 recording locations using Wildlife Acoustics Songmeter 3 (SM3) units during May 2015. Acoustic data were analysed using Kaleidoscope Pro, focusing on the left channel to avoid duplicate detections. The classifier scanned recordings for target sounds based on specified signal parameters and employed cluster analysis to categorise events. The classifier demonstrated high precision (95.1%) and a low false negative rate (0.5%), with most misclassifications due to other bird species with similar frequencies. The recall rate averaged 61%, varying from 40% to 100% across different recorders. Variations in recall rate were influenced by habitat structure, environmental noise, and distance between the target species and the recorder. This study highlights the potential of automated classifiers in ecoacoustic monitoring.

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: Forum Acousticum 2025
• Start Date: Jun 22, 2025
• End Date: Jun 26, 2025
• Acceptance Date: Apr 30, 2025
• Deposit Date: Jul 15, 2025
• Publicly Available Date: Jul 17, 2025
• Peer Reviewed: Peer Reviewed