A novel chicken voice recognition method using the orthogonal matching pursuit algorithm

In an ecological breeding environment, feeders usually don´t know whether their chicken are in good condition. Fortunately, Chicken voices reveal a lot of messages and it is easy to access and process voice signals. Therefore, we propose a new-type chicken voice recognition method using sparse representation aiming at the chicken voice recognition problem in an ecological breeding environment. In future, the method can be used in chickens for automated disease detection during the period of the bird flu. First, we use a multi-band spectral subtraction method for de-noise processing. Second, we reconstruct voice signals via sparse representation using the orthogonal matching pursuit algorithm. Third, we extract Mel-frequency cepstral coefficients (MFCC), linear predictive coding (LPC) and power-normalized cepstral coefficients (PNCC) from the chicken voices. Finally, we use support vector machine (SVM) to classify chicken voices under different environments. Extensive experimental results show that the features using sparse representation can respectively get better recognition effects.