Machine Learning Algorithms Can Predict Emotional Valence Across Ungulate Vocalizations

This repository contains supplemental data associated with the study. It includes the following documents:

Data S1: Contains 3,181 individual animal calls, labeled according to species, context of production, and emotional valence (i.e., negative or positive).

Data S2: Provides 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.

Data S3: Includes a database of extracted acoustic parameters, such as duration, frequency, amplitude modulation, spectral centroid, and more.

Data S4: Includes statistical summaries of acoustic features categorized by species and emotional valence, such as the number of observations, mean, standard deviation, median, minimum, maximum, range, skewness, kurtosis, standard error, and interquartile range.