Abstract:

Aims: Passive acoustic monitoring technology has been widely used for monitoring bird species, enabling non-invasive and long-term effective monitoring. Extensive data collection requires automated identification technologies for effective analysis. However, differences in recording device performance can affect the accuracy of automated software in identifying bird species.

Methods: Six separate recording devices from various manufacturers are tested by recording bird call playback across four frequency bands. We use BirdNET as the automatic bird sound identifier under two types of vegetation environment, five categories of distance between the recording devices and sound source, and three sound source directions. Our goal is evaluating the impact of these variables on bird species identification performance. We assess the monitoring performance of different recording devices by comparing the basic parameters and configurations of the devices and constructing a generalized linear model (GLM) to statistically analyze the identification results.

Results: Our analysis suggests the type of recording device significantly affects the ability for BirdNET to correctly identify bird species. As distance increases, the effectiveness of the devices in monitoring decreases, with the identification accuracy of BirdNET significantly higher for distances within 50 meters than beyond. Further, the direction of sound impacts identification performance, with accuracy significantly decreasing when the sound source is in opposite direction of the recording device in identifying the four types of bird sound signals with different frequency bandwidth ranges. Additionally, the vegetation type significantly affects the attenuation of bird call signals, with overall identification accuracy in grassland vegetation 40.1% higher than forest vegetation.

Conclusions: Our findings suggest the effectiveness of field recording monitoring should be assessed before selecting and deploying long-term recording monitoring equipment, in addition to evaluating equipment costs and parameters. Based on our evaluation, monitoring distance and direction settings should be optimized to enhance the effectiveness of monitoring strategies.

Key words: passive acoustic monitoring, acoustic recording devices, bird sound recognition, deep learning, BirdNET