Machine Learning Algorithms Can Predict Emotional Valence Across Ungulate Vocalizations
Machine Learning Algorithms Can Predict Emotional Valence Across Ungulate Vocalizations
This repository contains supplemental data associated with the study. It includes the following documents: SoundsDatabase.zip : A folder containing 3,181 individual audio recordings of animal contact calls. Each file is labeled with metadata including species, context of production, and emotional valence (positive/negative). Data S1: A spreadsheet with detailed information for each call, including animal ID, species, call type, emotional valence, context of production, number of calls, and references to the studies from which the calls were collected.
- Harvard University United States
- University of Copenhagen Denmark
vocalizations, sheep, goats, domesticated animal, acoustic correlates of emotion, animal emotion, pigs, emotions, non-invasive animal welfare assessment, animal welfare, bioacoustics, welfare, cows, machine learning, classification, ai, animal language, inter-species communication, wild boars, comparative emotional acoustics, przewalski's horses, horses
vocalizations, sheep, goats, domesticated animal, acoustic correlates of emotion, animal emotion, pigs, emotions, non-invasive animal welfare assessment, animal welfare, bioacoustics, welfare, cows, machine learning, classification, ai, animal language, inter-species communication, wild boars, comparative emotional acoustics, przewalski's horses, horses
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