基于隐马尔科夫模型的猪只状态自动识别技术  

Automatic recognition for pig states based on based on hidden Markov model

作　　者：张苏楠[1] 王芳[1] 阎高伟[1] 田建艳[1] 张振华[1] 

机构地区：[1]太原理工大学信息工程学院,太原030024

出　　处：《黑龙江畜牧兽医》2016年第11期97-99,103,294,共5页Heilongjiang Animal Science And veterinary Medicine

基　　金："863"国家高技术研究发展计划项目(2013AA102306)

摘　　要：为了实现猪只不同状态下声音的自动监测,试验采用声音识别技术,首先将猪只不同状态的声音信号进行双门限端点检测和预加重处理,然后通过大量试验对比,采用小波阈值法对声音信号进行去噪处理,并提取梅尔倒谱系数(MFCC)和一阶差分梅尔倒谱系数(ΔMFCC)作为描述特征,建立隐马尔可夫模型(HMM),最后对猪只不同状态的声音进行自动识别。结果表明:猪只状态识别精度较高,有助于提高自动监测系统的智能化判断能力。In the intensive breeding, automatic recognition technology for pig states under the background of complex noise has an important sig- nificance for automatic breeding. To achieve automatic monitoring of voice from pigs under eight states, sound - recognition technology was used in the test. Firstly, the pig＇s sound signals in different states were used for double - threshold endpoint detection and pre - emphasis, and then were used for de - noising treatment using the wavelet threshold method through a lot of experimen comparisons. Mel - frequency cepstrum coef- ficient （MFCC） and first - order difference MFCC were extracted as descriptive characteristics to establish a hidden markov model（HMM）. Fi- nally, the pig＇s sounds in different states were automatically recognized. The results showed that the recognition accuracy for pig states was rela- tively higher, and it was helpful to raise the capacity for intelligent judgment in the automatic system.

关 键 词：猪只状态检测 声音识别 隐马尔可夫模型(HMM) 梅尔倒谱系数(MFCC) 一阶差分梅尔倒谱系数 端点检测 预加重 小波阈值去噪 

分 类 号：S828[农业科学—畜牧学]