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Understanding Feline Communication: Automated Classification of Cat Vocalizations Using VGG16

S.M. Ashikur Rahman, Shirin Reza Medha, Oishi Singh, Noor E Fahmida, Ashadu Jaman Shawon

Abstract


This project presents an innovative system that utilizes a Convolutional Neural Network (CNN) architecture to classify and analyze cat vocalizations, determining their corresponding emotions. The system employs the VGG16 model, a renowned deep learning architecture, to accurately recognize and categorize cat sounds into distinct mood categories such as Angry, Defense, Fighting, Happy, Hunting Mind, Mating, Paining, Resting, and Warning. By focusing on voice analysis, the system aims to bridge the communication gap between cats and their owners, fostering a better understanding and improving the quality of pet care. The proposed approach extracts Mel-frequency cepstral coefficients (MFCCs) from the audio signals as input features and trains the CNN model on a comprehensive dataset of cat vocalizations. The system achieves an impressive accuracy of 88% on the validation set, demonstrating its effectiveness in interpreting feline emotions through vocal expressions.


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References


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