Experimental test of birdcall detection by autonomous recorder units and by human observers using broadcast

Abstract

Autonomous recording units are now routinely used to monitor birdsong, starting to supplement and potentially replace human listening methods. However, to date there has been very little systematic comparison of human and machine detection ability. We present an experiment based on broadcast calls of nocturnal New Zealand birds in an area of natural forest. The soundscape was monitored by both novice and experienced humans performing a call count, and autonomous recording units. We match records of when calls were broadcast with detections by both humans and machines, and construct a hierarchical generalized linear model of the binary variable of correct detection or not, with a set of covariates about the call (distance, sound direction, relative altitude, and line of sight) and about the listener (age, experience, and gender). The results show that machines and humans have similar listening ability. Humans are more homogeneous in their recording of sounds, and this was not affected by their individual experience or characteristics. Humans were affected by trial and location, in particular one of the stations located in a small but deep valley. Despite recorders being affected significantly more than people by distance, altitude, and line of sight, their overall detection probability was higher. The specific location of recorders seems to be the most important factor determining what they record, and we suggest that for best results more than one recorder (or at least, microphone) is needed at each station to ensure all bird sounds of interest are captured.

Detecting and Tracking the Positions of Wild Ungulates Using Sound Recordings

“…In contrast, the distance between point E and Rec 1 (i.e., the farthest recorder from point E among the three recorders) was 495 m, and the sound intensity at Rec 1 was very low, which caused an error in detecting the starting time of the whistle sound at Rec 1. Additionally, increasing the distance between the sound source and the sound recordings along with the presence of other environmental features (e.g., high density of trees or altitude difference) attenuate sound recordings and thereby diminishing the retrieval accuracy, which is consistent with the findings of Castro et al [42] and Priyadarshani et al [43]. This conclusion matches our inference in Section 2.4, that the probability of sound detection decreases from approximately 500 m.…”

Acoustic monitors and direct observations provide similar but distinct perspectives on bird assemblages in a lowland forest of eastern Ecuador

“…Monitors also may be more affected by interference from background noise (insects, primates) that make it difficult to identify vocalizations. Specific locations of monitors with respect to habitat or topography also may affect sound detection ( Castro et al, 2019 ) and thereby influence estimates of species composition.…”

Autonomous Installations for Monitoring the “Protector Prosperina“ Forest

“…Usually monitoring fauna species requires great field efforts to estimate for example bird diversity and their movement patterns. With an autonomous system for audio recording would shed light on animal movement ecology according to breeding, migrating and others overlooked behaviors of animals [35]. The information these devices gather contributes with more knowledge for biodiversity conservation.…”