| 基于被动水声信号的淡水鱼混合比例识别 |
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| 引用本文： | 黄汉英,杨咏文,李路,赵思明,熊善柏,涂群资.基于被动水声信号的淡水鱼混合比例识别[J].农业机械学报,2019,50(10):215-221. |
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| 作者姓名： | 黄汉英 杨咏文 李路 赵思明 熊善柏 涂群资 |
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| 作者单位： | 华中农业大学,华中农业大学,华中农业大学,华中农业大学,华中农业大学,华中农业大学 |
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| 基金项目： | 国家重点研发计划项目（2018YFC1604000）和国家现代农业产业技术体系建设专项资金项目（CARS-45-27） |
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| 摘 要： | 针对淡水鱼混合比例识别问题,以鳊鱼和鲫鱼为研究对象,通过水听器采集不同混合比例下的淡水鱼被动水声信号,利用butter函数进行信号预处理,分别提取短时平均能量、短时平均过零率、4层小波包分解频段能量、平均Mel频率倒谱系数、基于功率谱的主峰频率和主峰值等特征,构建特征向量,建立了基于主成分分析的支持向量机混合比例识别模型。分析了不同混合比例的淡水鱼水声信号之间的显著性差异,研究了主成分个数对模型识别率的影响。结果表明,平均Mel频率倒谱系数对淡水鱼混合比例识别效果最优,主成分个数为19时,平均识别正确率为96. 43%,Kappa系数为0. 96。
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| 关 键 词： | 淡水鱼 被动水声信号 比例识别 主成分分析 支持向量机 |
| 收稿时间： | 2019/3/26 0:00:00 |
Mixed Proportion Identification of Freshwater Fish Based on Passive Underwater Acoustic Signals |
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| HUANG Hanying,YANG Yongwen,LI Lu,ZHAO Siming,XIONG Shanbai and TU Qunzi.Mixed Proportion Identification of Freshwater Fish Based on Passive Underwater Acoustic Signals[J].Transactions of the Chinese Society of Agricultural Machinery,2019,50(10):215-221. |
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| Authors: | HUANG Hanying YANG Yongwen LI Lu ZHAO Siming XIONG Shanbai and TU Qunzi |
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| Institution: | Huazhong Agricultural University,Huazhong Agricultural University,Huazhong Agricultural University,Huazhong Agricultural University,Huazhong Agricultural University and Huazhong Agricultural University |
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| Abstract: | 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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| Keywords: | freshwater fish passive underwater acoustic signal proportions recognition principal component analysis support vector machine |
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