BirdNET can be as good as experts for acoustic bird monitoring in a European city

Abstract

BirdNET has become a leading tool for recognising bird species in audio recordings. However, its applicability in ecological research has been questioned over the sometimes large number of species falsely identified. Using species-specific confidence thresholds has been identified as a powerful approach to solving this issue. However, determining these thresholds is time and resource-consuming. While optimising the parameter setting of the algorithm could be an alternative strategy, the effect of parameter settings on the algorithms performance is not well understood. Here, we compared the species identification of BirdNET against expert identification using an acoustic dataset comprising 930 minutes of recordings collected in Munich, Germany. The performance of BirdNET was evaluated using three performance metrics: precision, recall, and F1-score. The metrics were calculated using 24 combinations of the parameters: week, sensitivity, and overlap at four temporal aggregations (pooling of data across time intervals) to also test the effects of recording length. We found that BirdNET closely matched expert identification, particularly when given more data (higher temporal aggregation, F1 score = 0.84) and when including the parameters week of the year, a suitable sensitivity, and an overlap of one to two seconds. Thus, while there are still limitations, using appropriate parameter settings and recording durations, BirdNET yields results comparable to experts without the need for time-consuming estimation of species-specific thresholds. This approach offers reliable presence-absence data in a fast and efficient way while species-specific thresholds are not readily available.