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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.

The precise detection of pig cough is a crucial step for establishing an early warning system for pig respiratory diseases. With regard to high precision pig cough recognition, feature extraction and selection are of importance. However, few studies have investigated both acoustic and visual features of pig vocalisations as input features. In this paper, we proposed a novel feature fusion method which fusing acoustic and visual features to achieve an enhanced pig cough recognition rate. We firstly extracted acoustic features from audio signals, including root-mean-square energy (RMS), mel-frequency cepstral coefficients (MFCCs), zero-crossing rates (ZCRs), spectral centroid, spectral roll-off, spectral flatness, spectral bandwidth and chroma. Then, constant-Q transform (CQT) spectrograms were employed to extract visual features involving local binary pattern (LBP) and histogram of gradient (HOG). Subsequently, a hybrid feature set was created by combining acoustic and visual features. In this stage, Pearson correlation coefficient (PCC), recursive feature elimination based on random forest (RF-RFE) and principal component analysis (PCA) were exploited for dimensionality reduction. Finally, support vector machine (SVM), random forest (RF) and k-nearest neighbours (KNN) classifiers were used to conduct a performance evaluation. It is shown that the fused acoustic features (Acoustic) combined with LBP and HOG (A-LH) achieved 96.45% pig cough accuracy. The results reveal that the fusion feature set outperforms acoustic and visual features alone. [Display omitted] • Both acoustic and visual features were investigated for pig cough. • A hybrid feature vector created by early feature fusion enhanced recognition performance of pig cough. • Feature selection and extraction were employed to choose the optimal feature set. • The proposed method reached an accuracy of 96.45%. [ABSTRACT FROM AUTHOR]