农业机械学报2026，Vol.57Issue(15)：24-35,74,13.DOI:10.6041/j.issn.1000-1298.2026.15.002
基于轻量化卷积神经网络的笼养鸡异常发声识别方法
Lightweight Convolutional Neural Network-based Method for Abnormal Vocalization Recognition in Cage-housed Chickens
摘要
Abstract
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.关键词
笼养鸡舍/鸡只发声识别/对数梅尔频谱图/轻量化卷积神经网络Key words
cage-house chicken/poultry vocalization recognition/log-Mel spectrogram/lightweight convolutional neural network分类
农业科技引用本文复制引用
唐瑜嵘,解彬彬,范甲骏,袁超,李尚民,童海兵,沈明霞..基于轻量化卷积神经网络的笼养鸡异常发声识别方法[J].农业机械学报,2026,57(15):24-35,74,13.基金项目
江苏省重点及面上项目(BE2022379) （BE2022379）
