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Study on a Pig Vocalization Classification Method Based on Multi-Feature Fusion.
Hou, Yuting; Li, Qifeng; Wang, Zuchao; Liu, Tonghai; He, Yuxiang; Li, Haiyan; Ren, Zhiyu; Guo, Xiaoli; Yang, Gan; Liu, Yu; Yu, Ligen.
Afiliación
  • Hou Y; Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
  • Li Q; School of Science, China University of Geosciences (Beijing), Beijing 100083, China.
  • Wang Z; Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
  • Liu T; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.
  • He Y; School of Science, China University of Geosciences (Beijing), Beijing 100083, China.
  • Li H; College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin 300384, China.
  • Ren Z; College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin 300384, China.
  • Guo X; Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
  • Yang G; Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
  • Liu Y; Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
  • Yu L; Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
Sensors (Basel) ; 24(2)2024 Jan 05.
Article en En | MEDLINE | ID: mdl-38257406
ABSTRACT
To improve the classification of pig vocalization using vocal signals and improve recognition accuracy, a pig vocalization classification method based on multi-feature fusion is proposed in this study. With the typical vocalization of pigs in large-scale breeding houses as the research object, short-time energy, frequency centroid, formant frequency and first-order difference, and Mel frequency cepstral coefficient and first-order difference were extracted as the fusion features. These fusion features were improved using principal component analysis. A pig vocalization classification model with a BP neural network optimized based on the genetic algorithm was constructed. The results showed that using the improved features to recognize pig grunting, squealing, and coughing, the average recognition accuracy was 93.2%; the recognition precisions were 87.9%, 98.1%, and 92.7%, respectively, with an average of 92.9%; and the recognition recalls were 92.0%, 99.1%, and 87.4%, respectively, with an average of 92.8%, which indicated that the proposed pig vocalization classification method had good recognition precision and recall, and could provide a reference for pig vocalization information feedback and automatic recognition.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tos / Reconocimiento en Psicología Límite: Animals Idioma: En Revista: Sensors (Basel) Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tos / Reconocimiento en Psicología Límite: Animals Idioma: En Revista: Sensors (Basel) Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza