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基于被動(dòng)水聲信號(hào)的淡水魚混合比例識(shí)別
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國(guó)家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2018YFC1604000)和國(guó)家現(xiàn)代農(nóng)業(yè)產(chǎn)業(yè)技術(shù)體系建設(shè)專項(xiàng)資金項(xiàng)目(CARS-45-27)


Mixed Proportion Identification of Freshwater Fish Based on Passive Underwater Acoustic Signals
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    摘要:

    針對(duì)淡水魚混合比例識(shí)別問題,,以鳊魚和鯽魚為研究對(duì)象,,通過水聽器采集不同混合比例下的淡水魚被動(dòng)水聲信號(hào),,利用butter函數(shù)進(jìn)行信號(hào)預(yù)處理,,分別提取短時(shí)平均能量,、短時(shí)平均過零率,、4層小波包分解頻段能量,、平均Mel頻率倒譜系數(shù),、基于功率譜的主峰頻率和主峰值等特征,,構(gòu)建特征向量,,建立了基于主成分分析的支持向量機(jī)混合比例識(shí)別模型。分析了不同混合比例的淡水魚水聲信號(hào)之間的顯著性差異,,研究了主成分個(gè)數(shù)對(duì)模型識(shí)別率的影響,。結(jié)果表明,平均Mel頻率倒譜系數(shù)對(duì)淡水魚混合比例識(shí)別效果最優(yōu),,主成分個(gè)數(shù)為19時(shí),,平均識(shí)別正確率為96.43%,Kappa系數(shù)為0.96,。

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    The rational polyculture and close cultivation of multispecies freshwater fish have great practical significance in aquaculture. Aiming to identify the mixed proportions of freshwater fish, bream fish and crucian carp were taken as the research object. The passive acoustic signals of different proportions of freshwater fish were collected by hydrophone. The butter function was used for signal preprocessing. Then shorttime average energy, shorttime average zerocrossing rate, four layer wavelet packet decomposition frequency band energy, average Mel cepstrum coefficient, main peak frequency and principal peaks based on power spectrum were extracted to construct eigenvectors. The support vector machine model based on principal component analysis was used to realize the mixed proportion identification. The significant differences among the acoustic signals of freshwater fish with different mixed proportions were analyzed, and the influences of the number of principal component on the recognition rate of the model were studied. The results showed that the average Mel cepstrum coefficient had the most significant effect on the mixed proportions recognition of freshwater fish, and the effect of proportional recognition was the best by selecting the first 19 principal components. The average accuracy rate was 96.43% and Kappa coefficient was 0.96.

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黃漢英,楊詠文,李路,趙思明,熊善柏,涂群資.基于被動(dòng)水聲信號(hào)的淡水魚混合比例識(shí)別[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2019,50(10):215-221. HUANG Hanying, YANG Yongwen, LI Lu, ZHAO Siming, XIONG Shanbai, TU Qunzi. Mixed Proportion Identification of Freshwater Fish Based on Passive Underwater Acoustic Signals[J]. Transactions of the Chinese Society for Agricultural Machinery,2019,50(10):215-221.

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  • 收稿日期:2019-03-26
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  • 在線發(fā)布日期: 2019-10-10
  • 出版日期: 2019-10-10
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