Machine learning based animal emotion classification using audio signals

2025-03-23

AI Summary

This work addresses the challenge of acoustic modeling under small-sample conditions in animal emotion recognition, proposing the first end-to-end audio-based emotion classification framework tailored to individual dogs. To overcome data scarcity, the method fuses Mel-spectrogram representations with time-domain statistical acoustic features and establishes a comparative multi-model framework comprising SVM, random forests, and shallow neural networks. Evaluated on a single-dog audio dataset, it achieves over 70% overall emotion classification accuracy. The study breaks through a key technical bottleneck in quantifying animal emotions from limited acoustic data, empirically validating the feasibility of fine-grained canine emotion discrimination using audio signals alone. It introduces a novel paradigm for interpretable, individualized animal affective computing—advancing both the understanding of animal intent in human–machine interaction and the development of robust, dog-specific emotional inference tools.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Humans and AI: Emotional Intelligence

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Classify dog emotions using audio signals via machine learning
• Enhance human-machine interfaces with emotion recognition tools
• Achieve over 70% accuracy in single-dog audio emotion classification

Innovation

• Machine learning for dog emotion classification
• Audio signal processing and recognition
• 70% accuracy in emotion detection