基于改进鸟鸣分析的物种自动识别

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI:10.1109/ICMLA.2016.0037

Joshua Knapp, Guangzhi Qu, Feng Zhang

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引用次数: 4

摘要

这项工作旨在提高基于物种识别的鸟鸣分析的准确性。我们打算通过创建更有效的鸟类音节分割算法(MIRS)来实现这一目标，使用基于支持向量机的分类器来训练IRS和MIRS的特征。实验结果表明了该算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Automatic Species Recognition Based on Improved Birdsong Analysis

This work seeks to improve upon the accuracy of birdsong analysis based species recognition. We intend to accomplish this by creating a more effective bird syllable segmentation algorithms (MIRS), Support Vector machine based classifiers are used to train the features of IRS and MIRS. The experimental results show the effectiveness of the proposed algorithm.

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2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)

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