Dempster Shafer distance-based multi-classifier fusion method for pig cough recognition

High precision pig cough recognition and low computational cost is of great importance for the realization of early warning of pig respiratory diseases.Numerous researchers have improved the recognition rate of pig cough sounds to a certain extent from feature selection and feature fusion perspectives.However,there is still a margin for the improvement in the accuracy and complexity of existing methods.Meanwhile,it is challenging to further enhance the precision of a single classifier.Therefore,this study proposed a multi-classifier fusion strategy based on Dempster Shafer distance(DS-distance)algorithm to increase the classification accuracy.Considering the engineering implementation,the machine learning with low computational complexity for fusion was chosen.First,three metrics of accuracy and diversity between classifiers were defined,including overall accuracy(OA),double fault(DF),and overall accuracy and double fault(OADF),for selecting the base classifiers.Subsequently,a two-step base classifier selection approach based on these metrics was proposed to make an optimized selection of features and classifiers.Finally,the proposed DS-distance algorithm was used to fuse the selected base classifiers to create a classification.The sound data collected in the pig barn verified the proposed algorithm.The experimental results revealed that the overall recognition accuracy of the proposed method could reach 98.76%,which was better than the existing methods.This study has achieved a high recognition accuracy through ensembled machine learning with low computational complexity.The proposed method provided an efficient way for the quick establishment of high precision pig cough recognition model in practice.

A new fusion feature based on convolutional neural network for pig cough recognition in field situations

Pig cough is considered the most common clinical symptom of respiratory diseases.Thus,establishing an early warning system for respiratory diseases in pigs by monitoring and identifying their cough sounds is important.In this paper,we propose a new fusion feature,namely Mel-frequency cepstral coefficient-convolutional neural network(MFCC-CNN),to improve the recognition accuracy of pig coughs.We obtained the MFCC-CNN feature by fusing multiple frames of MFCC with multiple one-layer CNNs.We used softmax and linear support vector machine(SVM)classifiers for classification.We tested the algorithm through field experiments.The results reveal that the performance of classifiers using the MFCC-CNN feature was significantly better than those using the MFCC feature.The F1-score increased by 10.37%and 5.21%,and the cough accuracy increased by 7.21%and 3.86%for the softmax and SVM classifiers,respectively.We also analyzed the impact of different numbers of fusion frames on the classification performance.The results reveal that fusing 55 and 45 adjacent frames resulted in the best performance for the softmax and SVM classifiers,respectively.From this research,we can conclude that a system constructed by simple one-layer CNNs and SVM classifiers can demonstrate excellent performance in pig sound recognition.

Recognition of sick pig cough sounds based on convolutional neural network in field situations

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.