chatter: a Python library for applying information theory and AI/ML models to animal communication

TL;DR

chatter is a Python library that uses information theory and machine learning to analyze animal communication in a continuous latent space, capturing complexity beyond traditional categorization methods.

Contribution

it introduces a flexible, taxonomy-agnostic tool that models vocal sequences as trajectories in high-dimensional space using modern AI architectures.

Findings

tested on bird, bat, whale, and primate vocalizations

enables quantification of complexity, predictability, and novelty

bypasses manual categorization of units

Abstract

The study of animal communication often involves categorizing units into types (e.g. syllables in songbirds, or notes in humpback whales). While this approach is useful in many cases, it necessarily flattens the complexity and nuance present in real communication systems. chatter is a new Python library for analyzing animal communication in continuous latent space using information theory and modern machine learning techniques. It is taxonomically agnostic, and has been tested with the vocalizations of birds, bats, whales, and primates. By leveraging a variety of different architectures, including variational autoencoders and vision transformers, chatter represents vocal sequences as trajectories in high-dimensional latent space, bypassing the need for manual or automatic categorization of units. The library provides an end-to-end workflow -- from preprocessing and segmentation to model training and feature extraction -- that enables researchers to quantify the complexity, predictability, similarity, and novelty of vocal sequences.