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 F₁ values of 96.86 %, 97.03 %, 96.71 %, and 96.87 %, respectively, with a weighted Cohen's kappa of 0.937 and an area under the receiver operating characteristic curve (ROC-AUC) of 0.986, using only 0.88 million parameters. Grad-CAM visualization verified the effectiveness of the dual-encoder architecture of CVC-Fusion in capturing key features and achieving information complementarity. After transfer learning, CVC-Fusion achieved accuracy, precision, recall, and F₁ values of 80.69 %, 80.70 %, 80.69 %, and 80.62 %, respectively, with a weighted Cohen's kappa of 0.607 and an ROC-AUC of 0.794 in a real-world environment. The results demonstrate the effectiveness of CVC-Fusion for recognizing chicken vocalizations under controlled laboratory conditions while highlighting that accurate heat-stress recognition using a single audio modality remains challenging in semiopen cage-free chicken houses.