Automated detection of bird songs continues to be a challenge: The case of warbleR and Megascops centralis (Chocó owl)

Abstract

Acoustic monitoring allows the evaluation of spatio-temporal changes in animal populations. However, analyzing large volumes of information is challenging. We evaluate the performance of a detection technique (autodetec function of the R warbleR package) to identify vocalizations of Megascops centralis, using 6877 one-minute recordings from 21 sites in the vicinity of the Jaguas dam, Andes of Antioquia Colombia., All vocalizations were manually annotated and two sites (597 recordings) with the highest number of records (49 and 34) were selected to evaluate the algorithm. The function was implemented with audios at two sampling rates (44 100 Hz and 22 050 Hz) and three amplitude thresholds (5, 10, and 20). We assessed the performance of this function in terms of its sensitivity and specificity, and we estimate the probability of detection of a signal according to its quality. Sensitivity and specificity showed great variation (0-0.48 and 0.5-0.98 respectively) and the probability of detection of a signal increased with its quality (poor: 0.12, medium: 0.27 andhigh: 0.64). Acoustic monitoring has an enormous potential, and its success depends, in part, on the availability of automatic recognition tools, that are open access and can be easily implemented. This development can be achieved by strengthening acoustic collections.