2021/02/01 by Dae-Hyun Jung, Na Yeon Kim, Sang Ho Moon +7 · 2 citations
Agricultural and Biological Sciences · Computer Science · Veterinary · #Animal Behavior and Welfare Studies #Food Supply Chain Traceability #Music and Audio Processing
paper · pdf · doi:10.3390/ani11020357
openalex publication_date 2021/02/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.