农业机械学报2018，Vol.49Issue(3)：179-186,8.DOI:10.6041/j.issn.1000-1298.2018.03.022
基于深度信念网络的猪咳嗽声识别
Recognition of Pig Cough Sound Based on Deep Belief Nets
摘要
Abstract
In the early stage,pig cough sound could be detected for early disease warning,and a method based on deep belief nets (DBN) was proposed to construct a pig cough sound recognition model.Pig sounds of Landrace pigs,including cough,sneeze,eating,scream,hum and shaking ears sounds were automatically recorded.The samples were preprocessed by speech enhancement algorithm based on a psychoacoustical model and speech endpoint detection algorithm based on short-time energy to reduce the noise and get useful parts of samples.Based on the dynamic time warping (DTW) algorithm,the shorttime energy characteristics were scaled to a 300-dimensional short-time energy feature vector,while the 24-dimensional MFCC characteristics were scaled to a 720-dimensional MFCC feature vector.And then the 300-dimensional short-time energy feature vector and the 720-dimensional MFCC feature vector were combined to construct a 1020-dimensional vector as the input of the deep belief nets.The number of neuron of the three hidden layers were set to be 42,17 and 7,respectively,so the pig sound recognition model based on DBN was finally designed to be 1020-42-17-7-2.The 5-fold cross validation experiment showed that recognition rate,error recognition rate and total recognition rate of the best experimental group were 94.12％,7.45％ and 93.21％,respectively.Furthermore,the first 479 principal components of 1020 dimcnsion fcature parameters were obtained by PCA dimensionality reduction.The recognition rate,error recognition rate and total recognition rate obtained better performance,and the best experimental group reached 95.80％,6.83％ and 94.29％,respectively.The result demonstrated that the DBN model was effective for the pig cough recognition.关键词
生猪/咳嗽/深度信念网络/特征参数/识别Key words
pig/cough/deep belief nets/feature parameters/recognition分类
信息技术与安全科学引用本文复制引用
黎煊,赵建,高云,雷明刚,刘望宏,龚永杰..基于深度信念网络的猪咳嗽声识别[J].农业机械学报,2018,49(3):179-186,8.基金项目
华中农业大学大北农青年学者提升专项项目(2017DBN005)、现代农业产业技术体系项目(CARS-36)、国家重点研发计划项目(2016YFD0500506)和中央高校基本科研业务费专项资金项目(2015PY079) （2017DBN005）
