End-to-end detection of cough and snore based on ResNet18-TF for breeder laying hens: A field study

Cough and snore are the most representative vocalizations for chicken respiratory diseases, which severely restrict poultry health due to highly contagious and lethal characteristics. Nighttime inspection by veterinarians is the foremost solution to identify bird respiratory symptoms during production. However, it is subjective, time-consuming, and labor-intensive. This study proposed a novel end-to-end model (ResNet18-TF) based on ResNet18 and a Time-Frequency Attention Mechanism (TFBlock) to automatically recognize chicken cough and snore using data collected in a commercial layer breeder house. In addition, a comparative analysis was conducted to evaluate the performance of different input features. The results revealed that LogFbank features exhibited superiority over MFCC features in the task of chicken sound recognition. By incorporating first-order and second-order delta features into LogFbank, the combination of ‘LogFbank+ΔLogFbank+ΔΔLogFbank’ further improved model recognition accuracy by 2.34 %. Additionally, the TFBlock structure enhanced the model's performance for recognizing coughs and snores. Specifically, the F1-score of MobileViTv3-TF, EfficientNetV2-TF, and ResNet18-TF models were increased by 1.30 %, 0.88 %, and 1.84 %, respectively, compared to their respective counterparts without TFBlock. ResNet18-TF achieved the highest accuracy, precision, recall, and F1-score, with 94.37 %, 94.59 %, 94.56 %, and 94.57 %, respectively. The generalization of ResNet18-TF in real production systems was validated, with precision and recall for detecting abnormal sounds (coughs and snores) reaching 92.97 % and 87.53 %, respectively. The proposed end-to-end model does not require denoising or endpoint detection processes, constructing an efficient and user-friendly pipeline of abnormal sound detection, which is highly suitable for practical deployment in poultry production systems. • This study proposed an automatic model for recognizing chicken coughs and snores. • ResNet18-TF achieved 94.37 % accuracy in detecting chicken coughs and snores. • TFBlock structure improved the chicken vocalization detection F1-score by 1.30 %. • LogFbank outperformed MFCC features in detecting chicken coughs and snores.