Behavior classification and recognition for facility breeding sheep based on acoustic signal weighted feature

Abstract: Sheep farming husbandry in western region of China has been developing in the manner of intensive and large-scale facility production. Due to the high density of sheep in house, any unusual behavior, such as sheep fighting, will cause a great loss if the sheep farmer is not aware of its happening and takes measures in time. Since the sound from sheep can not only reflect the status of sheep's health status but also can reflect its response to environment, the behaviors of sheep can be determined by monitoring the sound from the sheep house. This will provide a theoretical basis on evaluation of the welfare level of sheep raising and breeding. In this study, by establishing an audio signal acquisition system for sheep house based on wireless network, the sound signals from 20 sheep under 5 kinds of behaviors (fight, hunger, cough, bite, and search companions) were collected, and then these signals were processed and 720 clear and non-overlapping sound samples were selected in software of Audacity. Although Mel Frequency Cepstrum Coefficients (MFCC) has been widely used for feature extraction of animal sound signals due to its capacities of simulating the processing of speech by human ear and its better noise resistance, it neglects the different contribution of each feature component in characterizing the sound signals from the sheep house. Therefore, in this study, a weighted MFCC method was proposed based on the entropy value method to improve the recognition rate of sheep's sounds. The weighted MFCC with its first and second order differential was optimized to obtain a 19-dimension feature parameter via principal component analysis. The recognition model of support vector machine binary tree, in which parameters of four classifiers were worked out through grid optimization test, was completed according to the sonograms rendering of five different sheep behaviors. And then, these behaviors were recognized and classified respectively with improved MFCC, traditional MFCC and Linear Prediction Cepstrum Coefficient (LPCC). The results demonstrated that the average recognition rate with the improved MFCC for five different sheep behaviors was up to 83.6%, which was 14.0% and 26.7% higher respectively than MFCC and LPCC. The recognition rate of sheep fight and sheep cough were only 80.6% and 79.5%, respectively, because fight sound and cough sound had similar short outbreak characteristics. The bite sound recall rate was reached to 92.5%, showing that the bite sound was with distinguished feature and uneasy to be confused with other sounds. So, the improved MFCC showed the better performance in characterizing the sounds from sheep house, and in raising the recognition rate of sheep behaviors. Modern techniques of sound analysis have provided tools for analyzing and classifying animal sounds. Taking advantage of this, future bioacoustical research on welfare assessment should focus on comprehensive studies of a broad spectrum of animal specific distress vocalizations. Increasing precise attributions of such utterances to environments, behavioral contexts and relevant physiological parameters will lead to a deeper understanding of their meaning and significance with respect to well-being of farm animals.