Acoustic features of vocalization signal in poultry health monitoring

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.