蛋鸡发声音频数据库的构建与应用

蛋鸡发声含有丰富的机体信息,充分挖掘其声学特性,并利用其无接触、无应激的优点,为建立基于发声信息的蛋鸡养殖远程监测平台提供基础依据。该研究借助音频数字化处理技术和数据库管理平台,以海兰褐蛋鸡为例,搭建系统分别采集其在小规模（5只）饲养条件下的叫声信息及其体态行为。运用音频处理软件Adobe Auditionv1.0和音频分析软件Praat5.3提取蛋鸡发声特征参数,包括持续时间、基音频率、频谱质心、共振峰及其衍生的统计值,以此构建出蛋鸡发声音频数据库,在此基础上分别选取蛋鸡产蛋行为发声、鸣唱声和鸣叫声等典型发声行为对比分析。结果表明,蛋鸡产蛋行为发声与鸣唱声均为多次重复的、有节奏的、短促的音节...

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Bibliographic Details
Published in农业工程学报 Vol. 28; no. 24; pp. 150 - 156
Main Author 余礼根 滕光辉 李保明 劳凤丹 邢永正
Format Journal Article
LanguageChinese
Published 中国农业大学水利与土木工程学院,农业部设施农业工程重点实验室,北京 100083%中国农业大学网络中心,北京 100083 2012
Subjects
共振峰
动物
基音频率
蛋鸡
行为研究
音频记录
频谱质心
频谱质心
音频记录
基音频率
蛋鸡
行为研究
动物
共振峰
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ISSN1002-6819
DOI10.3969/j.issn.1002-6819.2012.24.021

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Summary:蛋鸡发声含有丰富的机体信息,充分挖掘其声学特性,并利用其无接触、无应激的优点,为建立基于发声信息的蛋鸡养殖远程监测平台提供基础依据。该研究借助音频数字化处理技术和数据库管理平台,以海兰褐蛋鸡为例,搭建系统分别采集其在小规模（5只）饲养条件下的叫声信息及其体态行为。运用音频处理软件Adobe Auditionv1.0和音频分析软件Praat5.3提取蛋鸡发声特征参数,包括持续时间、基音频率、频谱质心、共振峰及其衍生的统计值,以此构建出蛋鸡发声音频数据库,在此基础上分别选取蛋鸡产蛋行为发声、鸣唱声和鸣叫声等典型发声行为对比分析。结果表明,蛋鸡产蛋行为发声与鸣唱声均为多次重复的、有节奏的、短促的音节所构成（称其为句子）,前者先抑后扬、后者先扬后抑,句子的音节个数分别是7.8±2.0、15.2±7.7,但其时频域特征间存在着显著差异（P〈0.05）,与鸣叫声相比,其发声特征参数如频谱质心、共振峰等有着显著差异。研究表明,掌握蛋鸡发声的含义,有助于了解其行为特性、机体状态以及种群间的信息传递,并为蛋鸡行为特征识别与数字化监测平台的构建提供数据支持。
Bibliography:Particular states of mood or emotion of laying hens may be accompanied by specific behaviors, vocalization being one of them. Taking advantages of non-invasive monitoring of sound analysis, relevant acoustic features and modern techniques will promote to establish a remote-monitoring platform for laying hens＇ management. State-of-art digitalization tools and database management technologies were used to the research of behavioral observation and audio analysis. The animals used in this experiment were 5 laying hens （Hy-Line Variety Brown, U.S. standard） and were housed in a feed anticipating condition. During 28 April--30 April 2012, the vocalizations of laying hens were recorded in early morning. A NI-PXI platform integrated with sound sensors were used for all the recordings. The acoustic features including duration of call, pitch, energy center of call, formants and the derivatives were derived using Adobe Audition vl.0 and Praat 5.3. All the features were calculated and written in audio database of laying
ISSN:1002-6819
DOI:10.3969/j.issn.1002-6819.2012.24.021