摘要
Coughing is an obvious respiratory disease symptom,which affects the airways and lungs of pigs.In pig houses,continuous online monitoring of cough sounds can be used to build an intelligent alarm system for disease early detection.Owing to complicated interferences in piggery,recognition of pig cough sound becomes difficult.Although a lot of algorithms have been proposed to recognize the pig cough sounds,the recognition accuracy in field sit-uations still needs enhancement.The purpose of this research is to provide a highly accu-rate pig cough recognition method for the respiratory disease alarm system.We propose a classification algorithm based on the fine-tuned AlexNet model and feature of the spectro-gram.With the advantages of the convolutional neural network in image recognition,the sound signals are converted into spectrogram images for recognition,to enhance the accu-racy.We compare the proposed algorithm’s performance with the probabilistic neural net-work classifier and some existing algorithms.The results reveal that the proposed algorithm significantly outperforms the other algorithms-cough and overall recognition accuracies reach to 96.8%and 95.4%,respectively,with 96.2%F1-score achieved.
基金
This work was supported by the grant from the National Key Research and Development Program of China under Grant 2016YFD0700204-02
the Earmarked Fund for China Agricul-ture Research System under Grant CARS-35
the"Young Talents"Project of Northeast Agricultural University under Grant 17QC20
the University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province under Grant UNPYSCT-2020092 and UNPYSCT-2018142
and the Hei-longjiang Post-doctoral Subsidy Project of China under Grant LBH-Z17020.
