Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires
This paper introduces a set of computational methods using unsupervised latent models to analyze animal vocalizations, projecting them into low-dimensional feature spaces learned from spectrograms. These methods reveal features like individual and species identity, geographic variation, and sequential organization across diverse species, offering new insights into animal communication and demonstrating the potential of latent models for comparative analyses and hypothesis testing.