Feature embeddings from the BirdNET algorithm provide insights into avian ecology

Abstract

Bioacoustics has become widely used in the study of acoustically active animals, and machine learning algorithms have emerged as efficient and effective strategies to identify species vocalizations. Current applications of machine learning in bioacoustics often identify acoustic events to the species-level but fail to capture the complex acoustic repertoires animals use to communicate, which can inform habitat associations, demography, behavior, and the life history of cryptic species. The penultimate layer of most machine learning algorithms results in a vector of numbers describing the input, called feature embeddings. Here, we demonstrate that the feature embeddings generated by the BirdNET algorithm can enable within-species classifications of acoustic events. First, we successfully differentiated adult and juvenile Great Gray Owls; second, we identified three unique sounds associated with Great Spotted Woodpeckers (series call, alarm call, and drumming). These applications of BirdNET feature embeddings suggest that researchers can classify vocalizations into groups when group membership is unknown, and that within-species grouping is possible even when target signals are extremely rare. These applications of a relatively “black-box” aspect of machine learning algorithms can be used to derive ecologically informative acoustic classifications, which can inform the conservation of cryptic and otherwise difficult to study species.