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.

Pig welfare is closely related to the economical production of pig farms. With regard to pig welfare assessment, pig sounds are significant indicators, which can reflect the quality of the barn environment, the physical condition and the health of pigs. Therefore, pig sound analysis is of high priority and necessary. In this review, the relationship between pig sound and welfare was analyzed. Three kinds of pig sounds are closely related to pig welfare, including coughs, screams, and grunts. Subsequently, both wearable and non-contact sensors were briefly described in two aspects of advantages and disadvantages. Based on the advantages and feasibility of microphone sensors in contactless way, the existing techniques for processing pig sounds were elaborated and evaluated for further in-depth research from three aspects: sound recording and labeling, feature extraction, and sound classification. Finally, the challenges and opportunities of pig sound research were discussed for the ultimate purpose of precision livestock farming (PLF) in four ways: concerning sound monitoring technologies, individual pig welfare monitoring, commercial applications and pig farmers. In summary, it was found that most of the current researches on pig sound recognition tasks focused on the selection of classifiers and algorithm improvement, while fewer research was conducted on sound labeling and feature extraction. Meanwhile, pig sound recognition faces some challenging problems, involving the difficulty in obtaining the audio data from different pig growth stages and verifying the developed algorithms in a variety of pig farms. Overall, it is suggested that technologies involved in the automatic identification process should be explored in depth. In the future, strengthen cooperation among cross-disciplinary experts to promote the development and application of PLF is also nessary.