基于轻量化卷积神经网络的笼养鸡异常发声识别方法OA

Aiming to address the limited stability of abnormal vocalization recognition in caged layer houses under complex acoustic conditions,where strong background noise,scarce abnormal samples,and class imbalance pose major challenges,a lightweight convolutional neural network(CNN)for three-class chicken sound classification was proposed.A dataset of 691 audio clips,including calls,sneeze-like sounds,and other sounds,was constructed from recordings collected in commercial hen houses and divided into training,validation,and test sets.Each clip was converted into a 128×130 log-Mel spectrogram as model input.A lightweight SimpleCNN was developed and compared with Inception V3 and ResNet-50.Results showed that SimpleCNN achieved an overall test accuracy of 0.95,with F1-scores of 0.95,0.91,and 0.98 for calls,sneeze-like sounds,and other sounds,respectively,demonstrating strong recognition performance for both the minority abnormal class and the noise class.In contrast,Inception V3 and ResNet-50 achieved overall accuracies of 0.56 and 0.32,respectively,and showed clear class bias,with ResNet-50 exhibiting class collapse.Confusion matrix and interpretability analyses further indicated that SimpleCNN more effectively focused on key time-frequency regions of target vocal events and reduced confusion between sneeze-like sounds and other sounds.These findings suggested that lightweight CNNs offered robust discrimination and practical deployment potential under small-sample,high-noise conditions,providing technical support for abnormal sound monitoring and non-contact health warning in layer houses.