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Artificial intelligence techniques have been used to classify automatically animal vocalizations. The researchers have found distinct acoustic patterns from the analysis of the relationship between regular and irregular components of the signal. In this work, we analyzed the feasibility of training models for bark classification according to the emotional state of the dog using a model designed to represent human emotions. For this, we induced positive and negative emotions in 10 Schnauzer and 10 Chihuahua dogs reacting to the presence of a stranger and playing with its owner. We recorded and analyzed almost six thousand audio samples of dog vocalizations. Human evaluators assessed the emotional status of dogs. We also found a strong correlation between the evaluators’ assessments and the automatic estimation of valence and arousal. Our study underlines the validity of a new technique to classify dog’s emotional states using an automatized acoustic analysis of its vocalizations that could be useful to model the relationship between sound properties and emotional content.

Assessment of the Emotional State in Domestic Dogs using a Bi-dimensional Model of Emotions and a Machine Learning Approach for the Analysis of its Vocalizations