基于发声特征和深度学习的白羽肉鸡全生命周期咳嗽检测方法  

Detection method for whole-life cycle cough of white-feathered broiler based on vocalization characteristics and deep learning

作　　者：袁超 沈明霞[2,4,5] 姚文 刘龙申[2,4,5] 陈佳 YUAN Chao;SHEN Mingxia;YAO Wen;LIU Longshen;CHEN Jia(College of Engineering,Nanjing Agricultural University,Nanjing 210031,China;College of Artificial Intelligence,Nanjing Agricultural University,Nanjing 210031,China;College of Animal Science&Technology,Nanjing Agricultural University,Nanjing 210095,China;Key Laboratory of Breeding Equipment,Ministry of Agriculture and Rural Affairs,Nanjing 210031,China;Jiangsu Smart Animal Husbandry Equipment Technology Innovation Center,Nanjing 210031,China)

机构地区：[1]南京农业大学工学院,江苏南京210031 [2]南京农业大学人工智能学院,江苏南京210031 [3]南京农业大学动物科技学院,江苏南京210095 [4]农业农村部养殖装备重点实验室,江苏南京210031 [5]江苏智慧牧业装备科技创新中心,江苏南京210031

出　　处：《南京农业大学学报》2023年第5期975-985,共11页Journal of Nanjing Agricultural University

基　　金：科技创新2030——“新一代人工智能”重大项目(2021ZD0113803);江苏省现代农机装备与技术示范推广项目(NJ2021-34)。

摘　　要：[目的]高密度养殖模式下笼养肉鸡呼吸系统疾病易发作且难防治,为此本文设计了一种基于发声特征和深度学习的白羽肉鸡全生命周期咳嗽检测方法,自动监测鸡只咳嗽发声,及时提供预警信息。[方法]对10~19日龄、20~29日龄、30日龄后3种不同生长阶段的鸡只发声信号进行数字滤波、谱减法去噪、端点检测等处理,提取滤波器组(filter bank,FBank)和梅尔频率倒谱系数(Mel-frequency cepstral coefficients,MFCC)特征,并与其各自一阶及二阶差分组合,作为VGG16与ResNet18神经网络的输入,完成咳嗽声、鸣叫声、其他声三分类模型训练。[结果]各日龄段利用不同发声特征与神经网络所构建的识别模型均能准确实现发声分类,在10~19日龄、20~29日龄FBank-VGG16模型效果较优,准确率分别为94.29%、97.65%,30日龄后MFCC-ResNet18模型准确率高于其他模型,为98.66%。随着日龄的增长,各模型的总体识别准确率均上升,增幅为3%~7%。[结论]本方法可快速准确对实际生产环境中不同生长阶段的鸡只咳嗽进行识别,为笼养鸡呼吸系统疾病的早期检测提供技术支撑。[Objectives]Under the high-density breeding mode,broilers in cages are prone to respiratory diseases,which are difficult to prevent.In this paper,a cough detection method for whole-life cycle cough of white-feathered broiler based on vocalization characteristics and deep learning was designed to automatically monitor the cough vocalization of broilers and provide early warning information in time.[Methods]Digital filtering,spectral subtraction denoising and endpoint detection were performed on the vocalization signals of broilers at three different growth stages:10-19 days old,20-29 days old and after 30 days of age,and the filter bank(FBank),Mel-frequency cepstral coefficients(MFCC)features and their respective first-order and second-order differences were used as the input of the VGG16 and ResNet18 neural networks to complete the three-category model training of cough,tweet and other vocalizations.[Results]The recognition models constructed by using different vocalization features and neural networks in each age group could accurately realize the vocalization classification,and the FBank-VGG16 model was better at 10-19 days old and 20-29 days old,and the accuracy rates were respectively 94.29%and 97.65%.The accuracy rate of the MFCC-ResNet18 model after 30 days was higher than other models at 98.66%.As the age increased,the overall recognition accuracy of each model increased,ranging from 3%to 7%.[Conclusions]This experimental method could quickly and accurately identify the cough sounds of broilers at different life stages in the actual production environment,and provided technical support for the early detection of respiratory diseases in caged broilers.

关 键 词：白羽肉鸡 咳嗽检测 滤波器组 梅尔频率倒谱系数 卷积神经网络 

分 类 号：S858.31[农业科学—临床兽医学]