Speech Emotion Recognition of Animal Vocals Using Deep Learning

This project explores the classification of animal emotions based on their vocalizations using deep learning models.

Leveraging the Kaggle dataset "Audio Cats and Dogs" and expanding it to include multiple animal species, the study employs

feature extraction, signal processing, and neural network architectures to analyse audio patterns. Advanced clustering

techniques and classification models are used to detect and categorize emotional states, enhancing our understanding of animal

communication. The project aims to achieve high classification accuracy and develop a robust model for real-time emotion

recognition in animals.