Explainable classification of goat vocalizations using convolutional neural networks.

ABSTRACT: Efficient precision livestock farming relies on having timely access to data and information that accurately describes both the animals and their surrounding environment. This paper advances classification of goat vocalizations leveraging a publicly available dataset recorded at diverse farms breeding different species. We developed a Convolutional Neural Network (CNN) architecture tailored for classifying goat vocalizations, yielding an average classification rate of 95.8% in discriminating various goat emotional states. To this end, we suitably augmented the existing dataset using pitch shifting and time stretching techniques boosting the robustness of the trained model. After thoroughly demonstrating the superiority of the designed architecture over the contrasting approaches, we provid

SUBMITTER: Ntalampiras S 

PROVIDER: S-EPMC11960873 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature