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. 2013 Sep 25;13(10):12929-42.
doi: 10.3390/s131012929.

Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems

Yongwha Chung  1 , Seunggeun Oh, Jonguk Lee, Daihee Park, Hong-Hee Chang, Suk Kim
Affiliations

Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems

Yongwha Chung et al. Sensors (Basel). .

Abstract

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.

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Figures

Figure 1.
Figure 1.
Flowchart of MFCC extraction procedure.
Figure 2.
Figure 2.
Overall structure of the pig wasting diseases detection and recognition system.
Figure 3.
Figure 3.
Picture of a pig house installed a stationary CCTV with an audio sensor.
Figure 4.
Figure 4.
Typical example of automatic pig sound acquisition process marked using the red rectangle.
Figure 5.
Figure 5.
Waveforms and spectrograms of pig wasting diseases cough sound and normal sound samples.
See this image and copyright information in PMC

References

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