闫丽,邵庆,吴晓梅,谢秋菊,孙昕,韦春波.基于偏度聚类的哺乳期母猪声音特征提取与分类识别[J].农业机械学报,2016,47(5):300-306.
Yan Li,Shao Qing,Wu Xiaomei,Xie Qiuju,Sun Xin,Wei Chunbo.Feature Extraction and Classification Based on Skewness Clustering Algorithm for Lactating Sow[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(5):300-306.
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基于偏度聚类的哺乳期母猪声音特征提取与分类识别   [下载全文]
Feature Extraction and Classification Based on Skewness Clustering Algorithm for Lactating Sow   [Download Pdf][in English]
投稿时间:2015-10-17  
DOI:10.6041/j.issn.1000-1298.2016.05.041
中文关键词:  哺乳母猪  声音识别  偏度  聚类  降维  哺乳模式
基金项目:黑龙江省青年科学基金项目(QC2014C078、QC2013C031)、黑龙江省教育厅科研项目(12541493)和大庆市指导性科技计划项目(szdfy-2015-23)
作者单位
闫丽 黑龙江八一农垦大学
南京农业大学 
邵庆 黑龙江八一农垦大学 
吴晓梅 国网黑龙江省电力公司黑河供电公司 
谢秋菊 黑龙江八一农垦大学 
孙昕 哈尔滨医科大学 
韦春波 黑龙江八一农垦大学 
中文摘要:哺乳期是母猪繁育仔猪的关键时期,哺乳母猪特有的发声是其生理、情绪健康及其对仔猪看护的母性能力的最直接表达。哺乳期间母猪所发声音种类众多,增加了快速定位及准确识别特定声音类型的复杂度,以小梅山母猪的哺乳声、饮水声、采食声及无食咀嚼声等常见声音为研究对象,以功率比作为特征向量,对频域进行更精细的能量计算,提出基于偏度的子带聚类法合并特征不显著的子带,减少特征向量数量,构建支持向量机(SVM)的声音分类识别器,统计各类声音的发声时长;进一步以单个哺乳周期为对象,建立成功哺乳的声音模式。试验结果表明,哺乳声、无食咀嚼声、采食声和饮水声的最大功率比分别位于[0Hz,1000Hz]、[1000Hz,1500Hz]、[1500Hz,2500Hz]和[2500Hz,8000Hz\]子带内,以4个子带的功率比为特征的声音判别模型的识别率分别为100%、100%、95.17%、96.61%,与等间隔子带划分及主成分分析法比较,减少了特征向量的数量,且显著提高了识别算法的精度,进一步应用在母猪分娩舍内,实现了对哺乳母猪的母性能力及其健康状况的无应激、实时监测。
Yan Li  Shao Qing  Wu Xiaomei  Xie Qiuju  Sun Xin  Wei Chunbo
Heilongjiang Bayi Agricultural University;Nanjing Agricultural University,Heilongjiang Bayi Agricultural University,Heihe Power Supply Company, State Grid Heilongjiang Electric Power Company Limited,Heilongjiang Bayi Agricultural University,Harbin Medical University and Heilongjiang Bayi Agricultural University
Key Words:lactating sow  vocalization recognition  skewness  clustering  dimension reduction  nursing mode
Abstract:The lactation period is a critical period for sows to breed their piglets, and the specific voice of lactating sows in this period is the most direct expression of their physiology, emotional health, and maternal ability to care for piglets. The rapid location and accurate identification will be more complex due to a variety of vocalizations during this period. Therefore, the vocalizations of nursing grunt, drinking, feeding and sham chewing were observed, and a fine energy calculation for frequency domain with a power ratio as a vector was carried out. Then, the sub band clustering method based on skewness was presented to merge the sub bands without significant characteristics to reduce the number of parameters. Thirdly, the recognizer for sow’s vocalizations was built based on support vector machine(SVM) to calculate the duration of the different types of vocalization. A sound mode of successful nursing was established further within single lactation circle. It is shown that the max power ratio frequency domain of the nursing grunt, the sham chawing, the feeding and the drinking are ranged from 0Hz to 1000Hz, 1000Hz to 1500Hz, 1500Hz to 2500Hz, and 2500Hz to 8000Hz, respectively. The accuracy of the vocalization recognition mode with these four sub bands power ratio frequency as parameters were 100%, 100%, 95.17% and 96.61%, respectively. Compared with the uniformly spaced sub band division and principal component analysis (PCA), the number of features was reduced, and the recognition accuracy was significantly improved in the clustering algorithm based on skewness. Thus, the proposed method could be further applied in the health and maternal ability of sows monitoring real timely and nonstressly.

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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