Automated classification of bird and amphibian calls using machine learning: A comparison of methods
Automated classification of bird and amphibian calls using machine learning: A comparison of methods
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DOI：
10.1016/j.ecoinf.2009.06.005
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发表时间：
2009-09-01
影响因子：
5.1
通讯作者：
Aide, T. Mitchell
中科院分区：
文献类型：
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作者：
Acevedo, Miguel A.;Corrada-Bravo, Carlos J.;Aide, T. Mitchell
我们比较了三种机器学习算法（线性判别分析、决策树和支持向量机）对九种青蛙和三种鸟类叫声的自动分类能力。此外，我们测试了两种描述每个呼叫的方法来训练/测试系统。呼叫用四个标准呼叫变量（最小和最大频率，呼叫持续时间和最大功率）或十一个变量来表征，其中包括三个标准呼叫变量（最小和最大频率，呼叫持续时间）和呼叫结构的粗略表示（呼叫的8段中最大功率的频率）。总共有10061个孤立的电话被用来训练/测试该系统。三种方法的平均真阳性率分别为：支持向量机法94.95%（平均假阳性率0.94%）、决策树法89.20%（平均假阳性率1.25%）、线性判别法71.45%（平均假阳性率1.98%）。基于4个或11个呼叫变量的分类精度没有统计学差异，但这种高效的数据约简技术与支持向量机的高分类精度相结合，是一种很有希望的声音自动物种识别组合。通过将自动数字记录系统与我们的自动分类技术相结合，我们可以大大增加生物多样性数据收集的时空覆盖范围。(C) 2009年Elsevier B.V.出版
We compared the ability of three machine learning algorithms (linear discriminant analysis, decision tree, and support vector machines) to automate the classification of calls of nine frogs and three bird species. In addition, we tested two ways of characterizing each call to train/test the system. Calls were characterized with four standard call variables (minimum and maximum frequencies, call duration and maximum power) or eleven variables that included three standard call variables (minimum and maximum frequencies, call duration) and a coarse representation of call structure (frequency of maximum power in eight segments of the call). A total of 10,061 isolated calls were used to train/test the system. The average true positive rates for the three methods were: 94.95% for support vector machine (0.94% average false positive rate), 89.20% for decision tree (1.25% average false positive rate) and 71.45% for linear discriminant analysis (1.98% average false positive rate). There was no statistical difference in classification accuracy based on 4 or 11 call variables, but this efficient data reduction technique in conjunction with the high classification accuracy of the SVM is a promising combination for automated species identification by sound. By combining automated digital recording systems with our automated classification technique, we can greatly increase the temporal and spatial coverage of biodiversity data collection. (C) 2009 Published by Elsevier B.V.