Method for detecting avian influenza disease of chickens based on sound analysis

Abstract

A new audio analysis-based detection method is put forward in this paper to detect avian influenza as early as possible. Because a distinctive difference exists in the short-time auto-correlation functions of recorded chicken sound and ambient noise, the former can be extracted from the record and the latter discarded. The mel-frequency cepstral coefficients (MFCC) are determined by analysing and processing the frequency domain of the extracted chicken sound signal. The coefficients serve as distinction criteria for healthy and infected chickens. The extracted chicken sound is trained and identified with a binary-classification support vector machine (SVM). The penalty parameter and kernel function parameter of the optimal SVM are determined through 4-fold cross validation. The maximum recognition rates for the verification set are 87.25% (for polynomial kernel function), 88.1250% (for radial basis function), and 87.1250% (for S-kernel function), respectively. According to the experimental results for the testing set, the accuracy rate of the avian-influenza detection method ranged between 84% and 90%. This work shows preliminary potential for fast and efficient detection of avian-influenza in large-scale poultry farming and poultry trading markets.