Semi-automatic classification of birdsong elements using a linear support vector machine.
Semi-automatic classification of birdsong elements using a linear support vector machine.
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DOI：
10.1371/journal.pone.0092584
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Okanoya K
中科院分区：
文献类型：
--
作者：
Tachibana RO;Oosugi N;Okanoya K
鸟鸣提供了一个独特的模型来理解复杂的序列行为背后的行为和神经基础。然而，鸟鸣分析需要艰苦的努力，使数据定量分析。以前的尝试已经成功地提供了一些减少人类的努力，参与鸟鸣片段分类。本研究的目的是进一步减少人类的努力，同时提高分类性能。在目前的建议中，线性核支持向量机被用来最大限度地减少人类生成的标签样本的数量，在鸟鸣声中进行可靠的元素分类，并使分类器能够处理高维声学特征，同时避免过拟合问题。孟加拉雀的歌声中有不同的元素（即，音节）被用作神经科学研究领域中的代表性测试案例。进行了三项评估，以测试（1）探索适当分类器设置的算法有效性和准确性，（2）减少指令数据集数量提供准确性的能力，以及（3）以最小化手动标记对大型数据集进行分类的能力。结果表明，该算法在歌曲音节分类中具有99.5%的可靠性。在评价（2）中，即使当将由人分类的指令数据减少到用于分类两分钟摘录的一分钟摘录（对应于300-400个音节）时，也确实保持了该精度。当使用全天记录的大型目标数据集（约30，000个音节）时，可靠性仍然相当，准确率为98.7%。使用线性核支持向量机在鸟鸣元素分类中显示出足够的准确性，并且最大限度地减少了手动生成的指令数据。所提出的方法将有助于减少在不牺牲可靠性的情况下，在鸟鸣分析费力的过程，因此可以帮助加快行为和研究使用鸣禽。
Birdsong provides a unique model for understanding the behavioral and neural bases underlying complex sequential behaviors. However, birdsong analyses require laborious effort to make the data quantitatively analyzable. The previous attempts had succeeded to provide some reduction of human efforts involved in birdsong segment classification. The present study was aimed to further reduce human efforts while increasing classification performance. In the current proposal, a linear-kernel support vector machine was employed to minimize the amount of human-generated label samples for reliable element classification in birdsong, and to enable the classifier to handle highly-dimensional acoustic features while avoiding the over-fitting problem. Bengalese finch's songs in which distinct elements (i.e., syllables) were aligned in a complex sequential pattern were used as a representative test case in the neuroscientific research field. Three evaluations were performed to test (1) algorithm validity and accuracy with exploring appropriate classifier settings, (2) capability to provide accuracy with reducing amount of instruction dataset, and (3) capability in classifying large dataset with minimized manual labeling. The results from the evaluation (1) showed that the algorithm is 99.5% reliable in song syllables classification. This accuracy was indeed maintained in evaluation (2), even when the instruction data classified by human were reduced to one-minute excerpt (corresponding to 300–400 syllables) for classifying two-minute excerpt. The reliability remained comparable, 98.7% accuracy, when a large target dataset of whole day recordings (∼30,000 syllables) was used. Use of a linear-kernel support vector machine showed sufficient accuracies with minimized manually generated instruction data in bird song element classification. The methodology proposed would help reducing laborious processes in birdsong analysis without sacrificing reliability, and therefore can help accelerating behavior and studies using songbirds.
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影响因子：
2.4
作者：
Chen, Zhixin;Maher, Robert C.
通讯作者：
Maher, Robert C.
DOI：
10.1196/annals.1298.026
发表时间：
2004-01-01
期刊：
BEHAVIORAL NEUROBIOLOGY OF BIRDSONG
影响因子：
--
作者：
Okanoya, K
通讯作者：
Okanoya, K
影响因子：
2.4
作者：
Anderson, SE;Dave, AS;Margoliash, D
通讯作者：
Margoliash, D
影响因子：
2.5
作者：
Tchernichovski, O;Nottebohm, F;Mitra, PP
通讯作者：
Mitra, PP
影响因子：
2.7
作者：
COHEN, J
通讯作者：
COHEN, J