In precision livestock farming, studies show that bird sound can be employed as a biomarker of health condition. One of the most important steps for this purpose is to study the feasibility of using acoustic features as criteria for disease diagnosis. In this research five acoustic features of bird calls were evaluated for determination of bird health condition. Signals were collected from broilers grown in three groups: control, challenged with Bronchitis, and challenged with Newcastle disease. Results of data analysis showed that, among the 5 acoustic features studied, wavelet entropy (WET) had the best performance and was able to detect Bronchitis on the third day after inoculation with 83% accuracy while the type II error in this test (incorrectly detecting sick bird as healthy) was less than 14% and 6% on the third day and fourth day, respectively. In the case of Newcastle disease, although WET and Mel cepstral coefficients (MFCC) exhibited similar accuracy (80% and 78% respectively on the fourth day), but the difference was that WET was more reliable in detecting healthy birds while MFCC had better performance detecting challenged birds.

在精确的畜牧业中，研究表明，鸟的声音可以用作健康状况的生物标记。为此目的最重要的步骤之一是研究使用声学特征作为疾病诊断标准的可行性。在这项研究中，对鸟类鸣叫的五个声学特征进行了评估，以确定鸟类的健康状况。从生长在三组中的肉鸡收集信号：对照组，支气管炎攻击和新城疫攻击。数据分析结果表明，在所研究的5个声学特征中，小波熵（WET）的性能最佳，能够在接种后第三天以83％的准确度检测出支气管炎，而该测试中的II型错误（检测不正确）在第三天和第四天，病鸟健康）分别低于14％和6％，分别。在纽卡斯尔病中，尽管WET和梅尔倒谱系数（MFCC）表现出相似的准确性（第四天分别为80％和78％），但是区别在于WET在检测健康鸟类方面更可靠，而MFCC具有更好的性能检测受到挑战的鸟类。