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。

英文摘要：

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 the14-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 backpropagation（ BP） neural network,and it＇s recognition rate,error recognition rate and total recognitionrate reached 92. 54%,5. 37% and 95. 04%,respectively. Therefore,the recognition results meet the requirement of the Dorper sheep cough sound recognition.