Abstract:Sound technology is an effective method to monitor animal behavior. Animal vocalization can reflect their individual health status and individual needs, and can be used as an assisted indicator for evaluating animal welfare and animal comfort level. In the process of laying hens’ breeding, it is helpful for farmers to understand their animals by effectively identifying different types of laying hens’ vocalization, so as to improve the production efficiency as well as animal welfare. A method of classification and recognition of Hy-Line Brown laying hens’ vocalization was introduced based on texture features of spectrogram. The method combined image processing with sound processing technology to analyze voiceprint information hiding in the two-dimensional spectrum of spectrograms from laying hens’ vocalization, and then the texture features were extracted from spectrogram by using 2D-Gabor filter. Subsequently, machine learning algorithm like backpropagation neural network was used for sound classification and recognition. Kinect for Windows V1 was selected as sound input device, and LabVIEW and Matlab software were used for developing the algorithm of sound data acquisition and sound analysis, respectively. The experimental results showed that the average precision rate and sensitivity rate were no less than 92.0%, and the sensitivity rate of fan noise was the highest one, which was 99.3%, and the sensitivity rate of normal calls was the lowest one, which was 76.0%. The research result can provide a visual and noninvasive method for farmers to identify the specific vocal behavior of laying hens, and also provide a feasible reference means for indepth study of animal behavior and welfare.
