Multidomain feature fusion for recognizing chicken vocalizations under heat stress.

Heat stress is a key factor influencing the production performance and welfare of chickens. Chicken vocalizations reflect their physiological and health status. Whether chickens are experiencing heat stress can be assessed by analyzing their vocalizations. In this study, a lightweight chicken vocalization classification-fusion (CVC-Fusion) model based on multidomain feature fusion was proposed to recognize chicken vocalizations under heat stress and thermoneutral conditions. CVC-Fusion uses two parallel feature extraction branches to model one-dimensional frequency-domain features and two-dimensional spectrogram features of chicken vocalizations. Time- and frequency-domain features were calculated from laboratory recordings collected at three rearing densities and five age groups to characterize differences in acoustic features under heat stress and thermoneutral conditions. An augmented laboratory chicken vocalization dataset was constructed to train and evaluate CVC-Fusion. Additionally, a real-world chicken vocalization dataset collected from a semiopen cage-free chicken house was used for transfer learning and performance evaluation. The results revealed that heat-stress vocalizations generally exhibited greater energy and more concentrated spectral distributions than thermoneutral vocalizations did. In the laboratory environment, CVC-Fusion achieved accuracy, precision, recall, and F