R. Dinesh Kumar, I. Bhavya Sri, G. Shiva Kumar, B. Lavan Kumar, O. Anil Kumar 02 7(1) The classification of animal vocal emotions is a burgeoning field with significant implications for animal welfare veterinary diagnostics and conservation driven by the need to accurately interpret emotional states from vocalizations Over 500000 vocal samples across 200 species are now cataloged in repositories like the Animal Vocalization Database AVD However existing manual analysis methods such as spectrographic analysis and acoustic feature scoring suffer from subjectivity with interobserver agreement averaging only 60 scalability issues due to timeintensive processes and noise interference reducing accuracy by up to 30 in field settings To address these challenges this study proposes a Deep Learning Convolutional Neural Network DLCNN for classifying four emotional classesanger disgust fear and purrin animal vocalizations Preprocessing involves MelFrequency Cepstral Coefficient MFCC feature extraction capturing spectral characteristics with 1340 coefficients per frame followed by noise reduction and normalization to ensure robustness Existing methods evaluated include Support Vector Machine SVM achieving 82 accuracy KNearest Neighbors KNN with 78 accuracy Decision Tree Classifier DTC AdaBoost and Linear Discriminant Analysis LDA These methods struggle with highdimensional MFCC data and complex emotional patterns often overfitting or underperforming on noisy inputs The proposed DLCNN architecture integrates multiple convolutional layers to extract spatial hierarchies followed by dense layers for classification trained with a categorical crossentropy loss function and optimized using Adam The DLCNN leverages data augmentation eg pitch shifting time stretching to enhance generalization achieving perfect performance metrics 100 accuracy precision recall and F1score across all classes This approach outperforms existing methods by capturing intricate acoustic patterns offering a scalable automated solution for realtime animal vocal emotion classification Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026