BattyCoda: A novel open-source software for bat call annotation and classification

Abstract

The field of acoustic communication needs tools that facilitate the annotation and labeling of animal calls. Bat acoustic libraries gathered over the past few decades have primarily focused on compiling echolocation calls, which have been leveraged to develop machine learning algorithms capable of classifying bat species. However, because these classification methods require large training datasets, they have not yet been generalized to classify types of bat communication calls. Communication call repertoires in bats are wide, and distinct syllables occur with varying frequency, with some call types being recorded only rarely. Furthermore, collecting communication calls poses greater technical challenges, making these calls more difficult to capture reliably. Here, we present BattyCoda, an open-access, customizable tool to categorize and label bat communication call types within the repertoire of a species using small training datasets (tens to hundreds of labeled calls). In this work, we compiled an initial training dataset of 11 types of big brown bat (Eptesicus fuscus) calls, tested the performance of various candidate classifiers, and assessed the final classifier's training sample size sensitivity. We found that the best performing classifier achieved a balanced accuracy of ∼50 %, with common call types achieving classification accuracies over 70 %. Our tool can greatly facilitate annotating bat calls in recordings by providing accurate labels for common call types, while also assisting researchers in categorizing rarer communication calls. BattyCoda has the potential to build research capacity in the field of acoustic communication by expanding the availability of libraries including a wider range of bat calls and species, thereby enabling the exploration of new hypotheses.