基于音频特征和模糊神经网络的禽流感病鸡检测
|
摘要
为了能在早期发现禽流感并进行预防,该文提出了一种基于音频特征和模糊神经网络的禽流感病鸡检测方法。依据获取的家禽音频和环境及其他噪声的谱熵差别大的特点,在复杂环境中分析并提取出鸡声,丢弃非鸡声段,对提取的鸡声进行分析及处理,计算短时过零率、短时能量以及短时过零率与短时能量混合特征,用作判别患禽流感的病鸡和健康鸡的依据。利用T-S模糊神经网络,对提取出来的家禽音频特征进行训练和识别,试验表明隶属度函数为钟形函数、隶属度个数为2时模糊神经网络对试验提取的3个鸡声特征组成的3组测试集的敏感性分别为75.47%、80.39%和76.92%,特异性分别为80.85%、79.59%和72.92%,正确识别率分别为78%、80%和75%。该研究为规模化家禽养殖场及大型家禽流通市场的禽流感病禽识别提供一套快速、高效检测方法。
Avian influenza influences the economy, food safety and human health. A rapid and accurate detection of chicken infected with avian influenza in farming not only directly benefits the chicken farming, but also prevents the cross propagation of avian influenza. This paper proposes a non-invasive disease poultry detection method based on voice analysis, which is designed to achieve the identification of the voice of chickens infected with avian influenza and that of the healthy ones. First, 14 white leghorn chickens of 5 weeks of age with specific pathogen free(SPF) were put into the isolated cage in the animal biosafety level 3(ABSL 3) laboratory to record their voice. The voice samples of healthy chickens were collected by a T&F-91 enhanced 32 G digital HD recording pen, and then the chickens were inoculated with the H7 N9 avian influenza virus in the ABSL-3 laboratory. The H7 N9 subtype avian influenza virus was diluted to 106 EID50/0.1 mL with 10 000 ?/mL penicillin and streptomycin free phosphate-buffered saline(PBS), which was then used to inoculate the chickens, each with 0.1 mL virus diluent. After that, the samples of infected chickens' voice were collected. Secondly, in light of the fact that the frequency of chickens' voice signal was higher than the ambient noise, the recorded voice signal was processed with pre-emphasis. The high pass filter was used, so as to weaken the signal of the noise and improve that of chickens' voice. Thirdly, the processed chicken voice signal was further treated with the hamming window, and then it was divided into smaller segment, 21.3 ms per frames, which could be regarded as quasi steady state process. Fourthly, because the spectral entropy values of the obtained chickens' voice and the noise were significantly distinguishing, the values of each frame were calculated out. Based on these values, the end point detection method was put forward, so that the chickens' voice fragments were extracted from the complex ambient noise-containing record, while the non-chicken voice was discarded. Fifthly, the extracted chickens' voice fragments were treated with time domain analysis, and 3 attributes(short time zero crossing rate, short time energy and the combination of them) were figured out as the characteristics of the healthy chickens and chickens infected with avian influenza. The 450 sampling voice of the healthy chickens and 450 of chicken infected with avian influenza were marked before their order being randomly disrupted. The marked samples were divided into 4 groups: 1 training set(600 samples) and 3 testing sets(100 samples in each group). Finally, the training set was trained by 3 Takagi-Sugeno(T-S) fuzzy neural networks(each with different types of the membership function: π function, Gaussian function and Bell function). It was revealed from the training result that the network with the bell function had the highest recognition rate. So the network with bell shape function was applied to the 3 testing sets and results were obtained respectively: the sensitivity was 75.47%, 80.39% and 76.92%, the specificity was 80.85%, 79.59% and 72.92%, and the true recognition rate was 78%, 80% and 75%. Therefore, this kind of detection method might provide a set of non-invasive, rapid and efficient methods for avian influenza infected chickens detection or identification in poultry farms and poultry circulation market.
Avian influenza influences the economy, food safety and human health. A rapid and accurate detection of chicken infected with avian influenza in farming not only directly benefits the chicken farming, but also prevents the cross propagation of avian influenza. This paper proposes a non-invasive disease poultry detection method based on voice analysis, which is designed to achieve the identification of the voice of chickens infected with avian influenza and that of the healthy ones. First, 14 white leghorn chickens of 5 weeks of age with specific pathogen free(SPF) were put into the isolated cage in the animal biosafety level 3(ABSL 3) laboratory to record their voice. The voice samples of healthy chickens were collected by a T&F-91 enhanced 32 G digital HD recording pen, and then the chickens were inoculated with the H7 N9 avian influenza virus in the ABSL-3 laboratory. The H7 N9 subtype avian influenza virus was diluted to 106 EID50/0.1 mL with 10 000 ?/mL penicillin and streptomycin free phosphate-buffered saline(PBS), which was then used to inoculate the chickens, each with 0.1 mL virus diluent. After that, the samples of infected chickens' voice were collected. Secondly, in light of the fact that the frequency of chickens' voice signal was higher than the ambient noise, the recorded voice signal was processed with pre-emphasis. The high pass filter was used, so as to weaken the signal of the noise and improve that of chickens' voice. Thirdly, the processed chicken voice signal was further treated with the hamming window, and then it was divided into smaller segment, 21.3 ms per frames, which could be regarded as quasi steady state process. Fourthly, because the spectral entropy values of the obtained chickens' voice and the noise were significantly distinguishing, the values of each frame were calculated out. Based on these values, the end point detection method was put forward, so that the chickens' voice fragments were extracted from the complex ambient noise-containing record, while the non-chicken voice was discarded. Fifthly, the extracted chickens' voice fragments were treated with time domain analysis, and 3 attributes(short time zero crossing rate, short time energy and the combination of them) were figured out as the characteristics of the healthy chickens and chickens infected with avian influenza. The 450 sampling voice of the healthy chickens and 450 of chicken infected with avian influenza were marked before their order being randomly disrupted. The marked samples were divided into 4 groups: 1 training set(600 samples) and 3 testing sets(100 samples in each group). Finally, the training set was trained by 3 Takagi-Sugeno(T-S) fuzzy neural networks(each with different types of the membership function: π function, Gaussian function and Bell function). It was revealed from the training result that the network with the bell function had the highest recognition rate. So the network with bell shape function was applied to the 3 testing sets and results were obtained respectively: the sensitivity was 75.47%, 80.39% and 76.92%, the specificity was 80.85%, 79.59% and 72.92%, and the true recognition rate was 78%, 80% and 75%. Therefore, this kind of detection method might provide a set of non-invasive, rapid and efficient methods for avian influenza infected chickens detection or identification in poultry farms and poultry circulation market.
引文
[1]Brown Mac,Moore Leslie,Mcmahon Benjamin,et al.Constructing rigorous and broad biosurveillance networks for detecting emerging zoonotic outbreaks[J].Plos One,2015,10(5):e124037.
[2]Mdbazlurr Mollah,Mda Hasan,Mda Salam,et al.Digital image analysis to estimate the live weight of broiler[J].Computers&Electronics in Agriculture,2010,72(1):48-52.
[3]Aydin A,Bahr C,Viazzi S,et al.A novel method to automatically measure the feed intake of broiler chickens by sound technology[J].Computers&Electronics in Agriculture,2014,101(1):17-23.
[4]Aydin A,Bahr C,Berckmans D.A real-time monitoring tool to automatically measure the feed intakes of multiple broiler chickens by sound analysis[J].Computers and Electronics in Agriculture,2015,114:1-6.
[5]浦雪峰,朱伟兴,陆晨芳.基于对称像素块识别的病猪行为监测系统[J].计算机工程,2009,35(21):250-252.Pu Xuefeng,Zhu Weixing,Lu Chenfang.Sick pig behavior monitor system based on symmetrical pixel block recognization[J].Computer Engineering,2009,35(21):250-252.(in Chinese with English abstract)
[6]Shao J,Xin H,Harmon J D.Comparison of image feature extraction for classification of swine thermal comfort behavior[J].Computers and Electronics in Agriculture,1998,19(3):223-232.
[7]Shao B,Xin H.A real-time computer vision assessment and control of thermal comfort for group-housed pigs[J].Computers and Electronics in Agriculture,2008,62(1):15-21.
[8]刘龙申,沈明霞,柏广宇,等.基于机器视觉的母猪分娩检测方法研究[J].农业机械学报,2014,45(3):237-242.Liu Longshen,Shen Mingxia,Bo Guangyu,et al.Sows parturition detection method based on machine vision[J].Transactions of the Chinese Society for Agricultural Machinery,2014,45(3):237-242.(in Chinese with English abstract)
[9]毕敏娜,张铁民,庄晓霖,等.基于色差信息多色彩模型的黄羽鸡快速分割方法[J].农业机械学报,2016,47(12):293-298.Bi Minna,Zhang Tiemin,Zhuang Xiaolin,et al.Fast segmentation method of yellow feather chicken based on difference of color information in different color models[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(12):293-298.(in Chinese with English abstract)
[10]毕敏娜,张铁民,庄晓霖,等.基于鸡头特征的病鸡识别方法研究[J].农业机械学报,2018,49(1):51-57.Bi Minna,Zhang Tiemin,Zhuang Xiaolin,et al.Recognition method of yellow feather chicken based on head features[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(1):51-57.(in Chinese with English abstract)
[11]Zhuang Xiaolin,Bi Minna,Guo Jilei,et al.Development of an early warning algorithm to detect sick broilers[J].Computers&Electronics in Agriculture,2018,144:102-113.
[12]王琳,孙传恒,李文勇,等.基于深度图像和BP神经网络的肉鸡体质量估测模型[J].农业工程学报,2017,33(13):199-205.Wang Lin,Sun Chuanheng,Li Wenyong,et al.Establishment of broiler quality estimation model based on depth image and BP neural network[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(13):199-205.(in Chinese with English abstract)
[13]劳凤丹,滕光辉,李军,等.机器视觉识别单只蛋鸡行为的方法[J].农业工程学报,2012,28(24):157-163.Lao Fengdan,Teng Guanghui,Li Jun,et al.Behavior recognition method for individual laying hen based on computer vision[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2012,28(24):157-163.(in Chinese with English abstract)
[14]刘恒,吴迪,苏家仪,等.运用高斯混合模型识别动物声音情绪[J].国外电子测量技术,2016,35(11):82-87.Liu Heng,Wu Di,Su Jiayi,et al.Recognition of animal sound’s emotion based on gaussian mixture model[J].Foreign Electronic Measurement Technology,2016,35(11):82-87.(in Chinese with English abstract)
[15]汪开英,赵晓洋,何勇.畜禽行为及生理信息的无损监测技术研究进展[J].农业工程学报,2017,33(20):197-209.Wang Kaiying,Zhao Xiaoyang,He Yong.Review on no moninavasive monitoring technology of poultry behavior and physiological information[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(20):197-209.(in Chinese with English abstract)
[16]于天福.基于声音特征的动物行为识别系统研究[D].哈尔滨:东北林业大学,2010.Yu Tianfu.The Research of Animal Behavior Recognition System Based on Sound Features[D].Harbin:Northeast Forestry University,2010.(in Chinese with English abstract)
[17]王丹聪.基于多传感器融合的猪只行为辨别[D].西安:陕西科技大学,2018.Wang Dancong.Pig Behavior Identification Based on Multisensor Fusion[D].Xi’an:Shaanxi University of Science and Technology,2018.(in Chinese with English abstract)
[18]Moura D J,Silva W T,Naas I A,et al.Real time computer stress monitoring of piglets using vocalization analysis[J].Computers and Electronics in Agriculture,2008,64(1):11-18.
[19]Moura D J,Silva W T,Naas I A,et al.Real time computer stress monitoring of piglets using vocalization analysis[J].Computers and Electronics in Agriculture,2008,64(1):11-18.
[20]Guarino M,Jans P,Costa A,et al.Field test of algorithm for automatic cough detection in pig houses[J].Computers and Electronics in Agriculture,2008,62(1):22-28.
[21]Ferrari S,Piccinini R,Silva M,et al.Cough sound description in relation to respiratory diseases in dairy calves[J].Preventive Veterinary Medicine,2010,96(3/4):276-280.
[22]Milone D H,Rufiner H L,Galli J R,et al.Computational method for segmentation and classification of ingestive sounds in sheep[J].Computers and Electronics in Agriculture,2009,65(2):228-237.
[23]Banakar Ahmad,Sadeghi Mohammad,Shushtari Abdolhamid.An intelligent device for diagnosing avian diseases:Newcastle,infectious bronchitis,avian influenza[J].Computers&Electronics in Agriculture,2016,127:744-753.
[24]曹晏飞.复杂背景下蛋鸡声音分类提取方法研究[D].北京:中国农业大学,2015.Cao Yanfei.Research on Methods for Classification and Extraction of Laying Hens'Vocalizations in Complex Environment[D].Beijing:China Agricultural University,2015.(in Chinese with English abstract)
[25]余礼根,滕光辉,李保明,等.蛋鸡发声音频数据库的构建与应用[J].农业工程学报,2012,28(24):150-156.Yu Ligen,Teng Guanghui,Li Baoming,et al.Development and application of audio database for laying hens[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2012,28(24):150-156.(in Chinese with English abstract)
[26]Oppenheim A V,Schafer R W,Buck W J R.Discrete-time signal processing[J].Electronics&Power,2009,23(2):157.
[27]Allen J B,Rabiner L.A unified approach to short-time Fourier analysis and synthesis[J].Proc IEEE,1977,65(11):1558-1564.
[28]Portnoff M.Short-time Fourier analysis of sampled speech[J].IEEE Transactions on Acoustics Speech&Signal Processing,1981,29(3):364-373.
[29]Immerseel L V,Peeters S.Digital implementation of linear gammatone filters:Comparison of design methods[J].Acoustics Research Letters Online,2003,4(3):59-64.
[30]Quatieri Thomas.Discrete-time Speech Signal Processing:Principles and Practice[M].Prentice Hall,2002.
[31]Shannon Claude E,Weaver Warren.The mathematical theory of communication[J].Computers in Medical Practice,1950,3(9):31-32.
[32]Shen J L,Hung J W,Lee L S."Robust entropy-based endpoint detection for speech recognition in noisy environments"[C]//The 5th International Conference on Spoken Language Processing,Incorporating the 7th Australian International Speech Science and Technology Conference,Sydney Convention Centre,Sydney,Australia,1998.
[33]Sun Zengqi,Xu Hongbin.Fuzzy neural network based on T Smodel[J].Journal of Tsinghua University,1997,37(3):76-80.
[34]Takagi T,Sugeno M.Fuzzy identification of systems and its applications to modeling and control[J].Readings in Fuzzy Sets for Intelligent Systems,1993,15(1):387-403.
[35]Jin Yaochu,Jiang J,Zhu J.Neural network based fuzzy identification and its application to modeling and control of complex systems[J].IEEE Transactions on Systems,Man and Cybernetics,1995,25(6):990-997.
[36]Takagi Tomohiro,Sugeno Michio.Fuzzy identification of systems and its applications to modeling and control[J].IEEETransactions on Systems Man&Cybernetics,1985,15(1):116-132.
[2]Mdbazlurr Mollah,Mda Hasan,Mda Salam,et al.Digital image analysis to estimate the live weight of broiler[J].Computers&Electronics in Agriculture,2010,72(1):48-52.
[3]Aydin A,Bahr C,Viazzi S,et al.A novel method to automatically measure the feed intake of broiler chickens by sound technology[J].Computers&Electronics in Agriculture,2014,101(1):17-23.
[4]Aydin A,Bahr C,Berckmans D.A real-time monitoring tool to automatically measure the feed intakes of multiple broiler chickens by sound analysis[J].Computers and Electronics in Agriculture,2015,114:1-6.
[5]浦雪峰,朱伟兴,陆晨芳.基于对称像素块识别的病猪行为监测系统[J].计算机工程,2009,35(21):250-252.Pu Xuefeng,Zhu Weixing,Lu Chenfang.Sick pig behavior monitor system based on symmetrical pixel block recognization[J].Computer Engineering,2009,35(21):250-252.(in Chinese with English abstract)
[6]Shao J,Xin H,Harmon J D.Comparison of image feature extraction for classification of swine thermal comfort behavior[J].Computers and Electronics in Agriculture,1998,19(3):223-232.
[7]Shao B,Xin H.A real-time computer vision assessment and control of thermal comfort for group-housed pigs[J].Computers and Electronics in Agriculture,2008,62(1):15-21.
[8]刘龙申,沈明霞,柏广宇,等.基于机器视觉的母猪分娩检测方法研究[J].农业机械学报,2014,45(3):237-242.Liu Longshen,Shen Mingxia,Bo Guangyu,et al.Sows parturition detection method based on machine vision[J].Transactions of the Chinese Society for Agricultural Machinery,2014,45(3):237-242.(in Chinese with English abstract)
[9]毕敏娜,张铁民,庄晓霖,等.基于色差信息多色彩模型的黄羽鸡快速分割方法[J].农业机械学报,2016,47(12):293-298.Bi Minna,Zhang Tiemin,Zhuang Xiaolin,et al.Fast segmentation method of yellow feather chicken based on difference of color information in different color models[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(12):293-298.(in Chinese with English abstract)
[10]毕敏娜,张铁民,庄晓霖,等.基于鸡头特征的病鸡识别方法研究[J].农业机械学报,2018,49(1):51-57.Bi Minna,Zhang Tiemin,Zhuang Xiaolin,et al.Recognition method of yellow feather chicken based on head features[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(1):51-57.(in Chinese with English abstract)
[11]Zhuang Xiaolin,Bi Minna,Guo Jilei,et al.Development of an early warning algorithm to detect sick broilers[J].Computers&Electronics in Agriculture,2018,144:102-113.
[12]王琳,孙传恒,李文勇,等.基于深度图像和BP神经网络的肉鸡体质量估测模型[J].农业工程学报,2017,33(13):199-205.Wang Lin,Sun Chuanheng,Li Wenyong,et al.Establishment of broiler quality estimation model based on depth image and BP neural network[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(13):199-205.(in Chinese with English abstract)
[13]劳凤丹,滕光辉,李军,等.机器视觉识别单只蛋鸡行为的方法[J].农业工程学报,2012,28(24):157-163.Lao Fengdan,Teng Guanghui,Li Jun,et al.Behavior recognition method for individual laying hen based on computer vision[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2012,28(24):157-163.(in Chinese with English abstract)
[14]刘恒,吴迪,苏家仪,等.运用高斯混合模型识别动物声音情绪[J].国外电子测量技术,2016,35(11):82-87.Liu Heng,Wu Di,Su Jiayi,et al.Recognition of animal sound’s emotion based on gaussian mixture model[J].Foreign Electronic Measurement Technology,2016,35(11):82-87.(in Chinese with English abstract)
[15]汪开英,赵晓洋,何勇.畜禽行为及生理信息的无损监测技术研究进展[J].农业工程学报,2017,33(20):197-209.Wang Kaiying,Zhao Xiaoyang,He Yong.Review on no moninavasive monitoring technology of poultry behavior and physiological information[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(20):197-209.(in Chinese with English abstract)
[16]于天福.基于声音特征的动物行为识别系统研究[D].哈尔滨:东北林业大学,2010.Yu Tianfu.The Research of Animal Behavior Recognition System Based on Sound Features[D].Harbin:Northeast Forestry University,2010.(in Chinese with English abstract)
[17]王丹聪.基于多传感器融合的猪只行为辨别[D].西安:陕西科技大学,2018.Wang Dancong.Pig Behavior Identification Based on Multisensor Fusion[D].Xi’an:Shaanxi University of Science and Technology,2018.(in Chinese with English abstract)
[18]Moura D J,Silva W T,Naas I A,et al.Real time computer stress monitoring of piglets using vocalization analysis[J].Computers and Electronics in Agriculture,2008,64(1):11-18.
[19]Moura D J,Silva W T,Naas I A,et al.Real time computer stress monitoring of piglets using vocalization analysis[J].Computers and Electronics in Agriculture,2008,64(1):11-18.
[20]Guarino M,Jans P,Costa A,et al.Field test of algorithm for automatic cough detection in pig houses[J].Computers and Electronics in Agriculture,2008,62(1):22-28.
[21]Ferrari S,Piccinini R,Silva M,et al.Cough sound description in relation to respiratory diseases in dairy calves[J].Preventive Veterinary Medicine,2010,96(3/4):276-280.
[22]Milone D H,Rufiner H L,Galli J R,et al.Computational method for segmentation and classification of ingestive sounds in sheep[J].Computers and Electronics in Agriculture,2009,65(2):228-237.
[23]Banakar Ahmad,Sadeghi Mohammad,Shushtari Abdolhamid.An intelligent device for diagnosing avian diseases:Newcastle,infectious bronchitis,avian influenza[J].Computers&Electronics in Agriculture,2016,127:744-753.
[24]曹晏飞.复杂背景下蛋鸡声音分类提取方法研究[D].北京:中国农业大学,2015.Cao Yanfei.Research on Methods for Classification and Extraction of Laying Hens'Vocalizations in Complex Environment[D].Beijing:China Agricultural University,2015.(in Chinese with English abstract)
[25]余礼根,滕光辉,李保明,等.蛋鸡发声音频数据库的构建与应用[J].农业工程学报,2012,28(24):150-156.Yu Ligen,Teng Guanghui,Li Baoming,et al.Development and application of audio database for laying hens[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2012,28(24):150-156.(in Chinese with English abstract)
[26]Oppenheim A V,Schafer R W,Buck W J R.Discrete-time signal processing[J].Electronics&Power,2009,23(2):157.
[27]Allen J B,Rabiner L.A unified approach to short-time Fourier analysis and synthesis[J].Proc IEEE,1977,65(11):1558-1564.
[28]Portnoff M.Short-time Fourier analysis of sampled speech[J].IEEE Transactions on Acoustics Speech&Signal Processing,1981,29(3):364-373.
[29]Immerseel L V,Peeters S.Digital implementation of linear gammatone filters:Comparison of design methods[J].Acoustics Research Letters Online,2003,4(3):59-64.
[30]Quatieri Thomas.Discrete-time Speech Signal Processing:Principles and Practice[M].Prentice Hall,2002.
[31]Shannon Claude E,Weaver Warren.The mathematical theory of communication[J].Computers in Medical Practice,1950,3(9):31-32.
[32]Shen J L,Hung J W,Lee L S."Robust entropy-based endpoint detection for speech recognition in noisy environments"[C]//The 5th International Conference on Spoken Language Processing,Incorporating the 7th Australian International Speech Science and Technology Conference,Sydney Convention Centre,Sydney,Australia,1998.
[33]Sun Zengqi,Xu Hongbin.Fuzzy neural network based on T Smodel[J].Journal of Tsinghua University,1997,37(3):76-80.
[34]Takagi T,Sugeno M.Fuzzy identification of systems and its applications to modeling and control[J].Readings in Fuzzy Sets for Intelligent Systems,1993,15(1):387-403.
[35]Jin Yaochu,Jiang J,Zhu J.Neural network based fuzzy identification and its application to modeling and control of complex systems[J].IEEE Transactions on Systems,Man and Cybernetics,1995,25(6):990-997.
[36]Takagi Tomohiro,Sugeno Michio.Fuzzy identification of systems and its applications to modeling and control[J].IEEETransactions on Systems Man&Cybernetics,1985,15(1):116-132.