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Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering.

Dae-Hyun JungNa Yeon KimSang Ho MoonChangho JhinHak-Jin KimJung-Seok YangHyoung Seok KimTaek Sung LeeJu Young LeeSoo Hyun Park
Published in: Animals : an open access journal from MDPI (2021)
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.
Keyphrases
  • deep learning
  • convolutional neural network
  • artificial intelligence
  • machine learning
  • big data
  • social media
  • high throughput
  • single cell