Machine learning based animal emotion classification using audio signals
This work addresses the need for more precise emotion classification tools in human-machine interfaces, but it is incremental as it applies existing machine learning methods to a specific domain.
The paper tackled the problem of automatically classifying a dog's emotional state from audio signals, achieving an overall accuracy above 70% for recordings from one dog.
This paper presents the machine learning approach to the automated classification of a dog's emotional state based on the processing and recognition of audio signals. It offers helpful information for improving human-machine interfaces and developing more precise tools for classifying emotions from acoustic data. The presented model demonstrates an overall accuracy value above 70% for audio signals recorded for one dog.