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.(c) 2020 China Agricultural University. Production and hosting by Elsevier B.V. on behalf of KeAi.This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/).