Time-series analysis for online recognition and localization of sick pig (Sus scrofa) cough sounds.
This paper considers the online localization of sick animals in pig houses. It presents an automated online recognition and localization procedure for sick pig cough sounds. The instantaneous energy of the signal is initially used to detect and extract individual sounds from a continuous recording and their duration is used as a preclassifier. Autoregression (AR) analysis is then employed to calculate an estimate of the sound signal, and the parameters of the estimated signal are subsequently evaluated to identify the sick cough sounds. It is shown that the distribution of just three AR parameters provides an adequate classifier for sick pig coughs. A localization technique based on the time difference of arrival is evaluated on field data and is shown that it is of acceptable accuracy for this particular application. The algorithm is applied on continuous recordings from a pig house to evaluate its effectiveness. The correct identification ratio ranged from 73% (27% false positive identifications) to 93% (7% false positive identifications) depending on the position of the microphone that was used for the recording. Although the false negative identifications are about 50% it is shown that this accuracy can be enough for the purpose of this tool. Finally, it is suggested that the presented application can be used to online monitor the welfare in a pig house, and provide early diagnosis of a cough hazard and faster treatment of sick animals.
Exadaktylos
Vasileios
V
Department of Biosystems, Division M3-BIORES Measure, Model & Manage Bioresponses, Catholic University of Leuven, Heverlee, Belgium.
Silva
Mitchell
M
Ferrari
Sara
S
Guarino
Marcella
M
Taylor
C James
CJ
Aerts
Jean-Marie
JM
Berckmans
Daniel
D
D000465:Algorithms; D000822:Animal Husbandry / Q000295:instrumentation*; D000818:Animals; D003371:Cough / Q000175:diagnosis* / Q000662:veterinary*; D003936:Diagnosis, Computer-Assisted / Q000662:veterinary*; D042241:Early Diagnosis; D008954:Models, Biological; D009862:Online Systems; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography / Q000662:veterinary; D034421:Sus scrofa; D013553:Swine Diseases / Q000175:diagnosis*; D013997:Time Factors