基于被动水声信号的淡水鱼种类识别  

Freshwater Fish Identification Based on Passive Underwater Acoustic Signals

作　　者：李路[1] 涂群资 黄汉英[1] 赵思明[2] 熊善柏[2] 马章宇 

机构地区：[1]华中农业大学工学院,武汉430070 [2]华中农业大学食品科技学院,武汉430070

出　　处：《农业机械学报》2017年第8期166-171,共6页Transactions of the Chinese Society for Agricultural Machinery

基　　金：中央高校基本科研业务费专项(2662015QC020;2662015PY078);国家现代农业产业技术体系建设专项(CARS-46-23)

摘　　要：针对淡水鱼种类自动识别问题,采用被动水声信号作为数据源,运用维纳滤波和采样降噪法对水声信号进行预处理,通过4层小波包分解算法提取频段能量,结合信号的短时平均能量和短时平均过零率构建特征向量,使用概率神经网络分类器实现了淡水鱼种类的快速识别,研究了不同平滑因子取值对识别效果的影响。结果表明,当平滑因子为9~19时识别效果最佳,其中草鱼、鳊鱼、鲫鱼的识别正确率均为100%,无鱼状态的识别正确率为77.3%,总正确率为94.3%。Aiming to identify freshwater fish species automatically,passive acoustic signal samples of common freshwater fish were collected by the HTI-96-MIN standard hydrophone. A wiener filter and a sampling noise reduction method were used to preprocess the samples. Then frequency band energy of the samples was extracted by using wavelet packet decomposition algorithm. The layer number of the algorithm was four. The characteristic vectors of the samples were comprised of short-time average energy,short-time average zero-crossing rate, frequency band energy, and difference among the characteristic vectors of the four classes samples. Furthermore,a probabilistic neural networkwas used to identify freshwater fish species rapidly. As different values of the smoothing factor σ,the identification effect was studied. The results indicated that the identification accuracy was the highest when the smooth factor was between 9 and 19. The identification accuracies of ctenopharyngodon idellus,megalobrama amblycephala and crucian carp were all 100%. The identification accuracy of passive acoustic signals with zero fish was 77. 3%. And the total accuracy was 94. 3%. The proposed freshwater fish identification method using passive underwater acoustic signals had higher accuracy and less calculation.It provided a new way for identifying freshwater fish species quickly.

关 键 词：淡水鱼 被动水声信号 种类识别 概率神经网络 

分 类 号：S24[农业科学—农业电气化与自动化] TB56[农业科学—农业工程]