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棲架養(yǎng)殖模式下蛋雞發(fā)聲分類識別
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國家自然科學基金資助項目(31072066);公益性行業(yè)(農業(yè))科研專項經費資助項目(201003011);現代農業(yè)產業(yè)技術體系建設專項資金資助項目(CARS—41)


Classification Methods of Vocalization for Laying Hens in Perch System
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    摘要:

    針對棲架養(yǎng)殖模式下蛋雞的發(fā)聲,采用頻譜分析技術,運用音頻分析軟件Sound Analysis Pro提取不同行為狀態(tài)下的發(fā)聲圖譜,采集其聲學參數作為特征向量,應用J48決策樹算法、樸素貝葉斯理論和支持向量機模型分別構建蛋雞發(fā)聲分類識別器,利用開源的數據挖掘平臺Weka 3.6進行實驗。結果表明,棲架養(yǎng)殖模式下,7:00~8:00的蛋雞發(fā)聲中,產蛋叫聲、愉悅叫聲分別占全部發(fā)聲的42.2%、21.6%,相比于傳統(tǒng)的籠養(yǎng)模式,有效地表達了蛋雞生長過程中的自然行為和生理活動;基于J48決策樹算法的蛋雞發(fā)聲分類模型識別率最高,達到88.3%,具有較好的識別效果,可運用于蛋雞發(fā)聲的實時監(jiān)測和不同情感的分類識別。

    Abstract:

    Multi-taper spectral analysis was used to perform vocalization classification for laying hens in perch system. Sound Analysis Pro software was applied to compute spectral derivatives and acoustic features. Three methods including J48 decision tree algorithm, NaiveBayes theory and support vector machine were used to classify sounds of laying hens by using the open source data mining tool of Weka 3.6. Experimental results showed that vocalization of egg laying process and pleasure notes accounted for 42.2% and 21.6% between 7:00~8:00, while the natural behaviors and physiological activities were strongly performed with a comparison to traditional cage system. It was found that J48 decision tree algorithm had the highest classification rate (88.3%) for vocalization of laying hens, which could be used for different animal vocalization.

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余禮根,滕光輝,李保明,勞鳳丹,曹晏飛.棲架養(yǎng)殖模式下蛋雞發(fā)聲分類識別[J].農業(yè)機械學報,2013,44(9):236-242. Yu Ligen, Teng Guanghui, Li Baoming, Lao Fengdan, Cao Yanfei. Classification Methods of Vocalization for Laying Hens in Perch System[J]. Transactions of the Chinese Society for Agricultural Machinery,2013,44(9):236-242.

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  • 在線發(fā)布日期: 2013-09-11
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