Animal Communication

Animal communication research aims to decipher the meaning and structure of non-human vocalizations, bridging the gap between human and animal languages. Current efforts utilize deep learning architectures, such as ensemble networks and generative models, coupled with techniques like wavelet scattering transforms and word embedding, to analyze diverse datasets including marine mammal sounds and dog vocalizations. These approaches, combined with causal inference methods, are revealing insights into the semantic content and underlying rules governing animal communication systems, potentially leading to a deeper understanding of animal cognition and behavior.