宣传忠,武佩,张丽娜,马彦华,张永安,邬娟.羊咳嗽声的特征参数提取与识别方法[J].农业机械学报,2016,47(3):342-348.
Xuan Chuanzhong,Wu Pei,Zhang Li’na,Ma Yanhua,Zhang Yongan,Wu Juan.Feature Parameters Extraction and Recognition Method of Sheep Cough Sound[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(3):342-348.
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羊咳嗽声的特征参数提取与识别方法   [下载全文]
Feature Parameters Extraction and Recognition Method of Sheep Cough Sound   [Download Pdf][in English]
投稿时间:2015-09-14  
DOI:10.6041/j.issn.1000-1298.2016.03.048
中文关键词:  杜泊羊  咳嗽声  特征参数提取  梅尔频率倒谱系数  隐马尔可夫模型
基金项目:“十二五”国家科技支撑计划项目(2014BAD08B05)、国家自然科学基金项目(11364029)、内蒙古自然科学基金项目(2012MS0720)、内蒙古“草原英才”产业创新人才团队项目(内组通字[2014]27号)和内蒙古农业大学科技创新团队项目(NDTD2013-6)
作者单位
宣传忠 内蒙古农业大学 
武佩 内蒙古农业大学 
张丽娜 内蒙古师范大学 
马彦华 内蒙古农业大学 
张永安 内蒙古农业大学 
邬娟 内蒙古农业大学 
中文摘要:为在设施圈养羊只产生呼吸道疾病的初期,通过监测其咳嗽声进行疾病预警和健康状况诊断,以内蒙古地区广泛推广的杜泊羊为例,对杜泊羊的咳嗽声信号进行自动采集和计算机识别,在不增加羊咳嗽声特征参数维数的前提下,提出一种改进的梅尔频率倒谱系数(MFCC),试验结果表明,该参数和短时能量、过零率组合的14维特征参数,经过羊咳嗽声隐马尔可夫模型(HMM)识别系统,其识别率、误识别率和总识别率分别达到了86.23%、7.17%和88.43%,该组合特征参数经主成分分析可降到9维,而通过BP神经网络改善的HMM咳嗽声识别系统,对咳嗽声的识别率、误识别率和总识别率分别达到了92.54%、5.37%和95.04%,满足了杜泊羊咳嗽声识别的要求。
Xuan Chuanzhong  Wu Pei  Zhang Li’na  Ma Yanhua  Zhang Yongan  Wu Juan
Inner Mongolia Agricultural University,Inner Mongolia Agricultural University,Inner Mongolia Normal University,Inner Mongolia Agricultural University,Inner Mongolia Agricultural University and Inner Mongolia Agricultural University
Key Words:Dorper sheep  cough sound  feature parameter extraction  Mel frequency cepstrum coefficient  hidden Markov model
Abstract:In farming region of Inner Mongolia, animal husbandry is evolving from the traditional style to the modern style, which means the large scale sheep breeding, intensive management and industrial development. However, the newly extensive stable breeding facilities are easily to make sheep suffer from respiratory disease. In the early stage, cough sound of sheep can be detected for early disease warning and health diagnosis. In this paper, taking Dorper sheep, which has been widely promoted in Inner Mongolia, for an example, cough sound signal of sheep was automatically collected and recognized by computer. Without increasing the dimension of sound signal feature parameters, an improved Mel frequency cepstrum coefficient (MFCC) was put forward. The experimental results demonstrated that the 14 dimensional parameters combined with improved MFCC, short time energy and zero crossing rate were used in the hidden Markov model (HMM) cough sound recognition system, whose recognition rate, error recognition rate and total recognition rate reached 86.23%, 7.17% and 88.43% respectively. And the combination parameters can be reduced to nine dimensions using principal components analysis (PCA) method. Furthermore, the cough sound recognition system based on HMM was enhanced by a back propagation (BP) neural network, and it’s recognition rate, error recognition rate and total recognition rate reached 92.54%, 5.37% and 95.04%, respectively. Therefore, the recognition results meet the requirement of the Dorper sheep cough sound recognition.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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