Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems.
Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. Further, respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this study, we propose an efficient data mining solution for the detection and recognition of pig wasting diseases using sound data in audio surveillance systems. In this method, we extract the Mel Frequency Cepstrum Coefficients (MFCC) from sound data with an automatic pig sound acquisition process, and use a hierarchical two-level structure: the Support Vector Data Description (SVDD) and the Sparse Representation Classifier (SRC) as an early anomaly detector and a respiratory disease classifier, respectively. Our experimental results show that this new method can be used to detect pig wasting diseases both economically (even a cheap microphone can be used) and accurately (94% detection and 91% classification accuracy), either as a standalone solution or to complement known methods to obtain a more accurate solution.
Department of Computer and Information Science, College of Science and Technology, Korea University, Sejong 339-700, Korea. dhpark@korea.ac.kr.
Yongwha
Y
Chung
Seunggeun
S
Oh
Jonguk
J
Lee
Daihee
D
Park
Hong-Hee
HH
Chang
Suk
S
Kim
D000818:Animals; D001314:Auscultation / Q000295:instrumentation / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011159:Population Surveillance / Q000379:methods*; D053570:Porcine Postweaning Multisystemic Wasting Syndrome / Q000175:diagnosis* / Q000503:physiopathology*; D015203:Reproducibility of Results; D012135:Respiratory Sounds / Q000503:physiopathology*; D012680:Sensitivity and Specificity; D013018:Sound Spectrography / Q000295:instrumentation / Q000379:methods*; D060388:Support Vector Machine; D013552:Swine