Classification of group-specific variations in songs within House Wren species using machine learning models

Abstract

Songbirds have shown variation in vocalizations across different populations and different geographical ranges. Such variations can over time lead to divergence in song characteristics, sometimes referred to as dialects. House Wren (Troglodytes aedon) is one such widely distributed bird species that has shown variation in its song characteristics within different populations. Traditionally, such studies have been conducted using manual approaches for classification. In this work we explore the use of machine learning models that can assist in performing classification of bird songs at a conspecific level. Two machine learning techniques, the random forest and a shallow feed forward neural network, are fed with pre-computed sound features to classify vocal variation in House Wren species across different reported population groups and latitudinal areas. A randomized approach is employed to create balanced subsets of sounds from different locations for repeated classification runs in order to provide a reliable estimate of performance. It is observed that such an automated approach is able to classify variations in songs within House Wren with high accuracy. We were also able to confirm the latitudinal variation of House Wren songs reported in previous studies. Given these results, we believe, such a purely data-driven way of analyzing bird songs in general can provide useful hints to biologists on where to look for interesting patterns in order to understand the evolutionary divergence in song characteristics.