With the rapid development of large-scale breeding, manual long-term monitoring of the daily activities and health of livestock is costly and time-consuming. Therefore, the application of bio-acoustics to automatic monitoring has received increasing attention. Although bio-acoustical techniques have been applied to the recognition of animal sounds in many studies, there is a dearth of studies on the automatic recognition of abnormal sounds from farm animals. In this study, an automatic detection and recognition system based on bio-acoustics is proposed to hierarchically recognize abnormal animal states in a large-scale pig breeding environment. In this system, we extracted the mel-frequency cepstral coefficients (MFCC) and subband spectrum centroid (SSC) as composite feature parameters. At the first level, support vector data description (SVDD) is used to detect abnormal sounds in the acoustic data. At the second level, a back-propagation neural network (BPNN) is used to classify five kinds of abnormal sounds in pigs. Furthermore, improved spectral subtraction is developed to reduce the noise interference as much as possible. Experimental results show that the average detection accuracy and the average recognition accuracy of the proposed system are 94.2% and 95.4%, respectively. The effectiveness of the proposed sound detection and recognition system was also verified through tests at a pig farm.
All Science Journal Classification (ASJC) codes
- Food Science
- Biomedical Engineering
- Agronomy and Crop Science
- Soil Science