AI Decodes Animal Emotions Through Vocal Patterns, Study Finds

A study conducted by the Department of Biology at the University of Copenhagen, Denmark, has demonstrated that artificial intelligence (AI) can decode animal emotions. Researchers trained a machine learning model to analyze vocal patterns and successfully distinguish between positive and negative emotions in seven species of ungulates, including cows, pigs, and wild boars, achieving an accuracy of 89.49%. This marks the first study to use AI for detecting emotional valence across multiple species.

According to the researchers, this breakthrough proves that AI can decode animal emotions through vocalization patterns, paving the way for real-time emotion monitoring in animals. This advancement has the potential to revolutionize animal welfare, livestock management, and conservation efforts.

The research team analyzed thousands of animal vocalizations recorded in different emotional states and identified key acoustic markers of emotional valence, including sound duration, energy distribution, fundamental frequency, and amplitude modulation variations. These vocal patterns were found to be consistent across species, suggesting that the vocal expression of emotions has been evolutionarily conserved.

Beyond its applications in animal welfare, this study offers insights into the evolutionary origins of human language and may reshape our understanding of animal emotions.