| 基于MFCC与神经网络的小蠹声音种类自动鉴别 |
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| 引用本文： | 罗茜,王鸿斌,张真,孔祥波.基于MFCC与神经网络的小蠹声音种类自动鉴别[J].北京林业大学学报,2011,33(5):81-85. |
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| 作者姓名： | 罗茜 王鸿斌 张真 孔祥波 |
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| 作者单位： | 中国林业科学研究院森林生态环境与保护研究所，国家林业局森林保护学重点实验室 |
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| 基金项目： | “863”国家高技术研究发展计划项目(2006AA10Z211); 中国林业科学研究院基本科研业务专项(CAFRIF200710) |
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| 摘 要： | 昆虫发出的各种声音具有种间特异性,是非常可靠的分类依据。利用这一特性,本实验旨在探索一种对昆虫自动分类的新方法。本实验录制了红脂大小蠹、云南切梢小蠹、短毛切梢小蠹和华山松大小蠹4种小蠹虫的胁迫声,利用Adobe Adition2.0对每个声音文件进行降噪,再将其截取成只含有一个脉冲组的声音片段。在MATLAB环境下对这...
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| 关 键 词： | 小蠹 声音 识别 神经网络 MFCC |
| 收稿时间： | 1900-01-01 |
Automatic stridulation identification of bark beetles based on MFCC and BP Network. |
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| LUO Qian, WANG Hong-bin, ZHANG Zhen, KONG Xiang-bo.Automatic stridulation identification of bark beetles based on MFCC and BP Network.[J].Journal of Beijing Forestry University,2011,33(5):81-85. |
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| Authors: | LUO Qian WANG Hong-bin ZHANG Zhen KONG Xiang-bo |
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| Institution: | Key Laboratory of Forest Protection of State Forestry Administration, Research Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing, 100091, P.R. China. |
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| Abstract: | The acoustic signals produced by insects are species-specific, and therefore can be employed for detection and identification purposes. This experiment aims to find a new way to automatically identify insects by their acoustic characteristics. Stridulation signals of four bark beetles, Dendroctonus valens, Tomicus yunnanensis, T. brevipilosus and D. armandi, were recorded under stress condition. Then after noise reduction, the signals were cut into one-echeme segments by audio software Adobe Adition2.0. With MATLAB7.1, borders of all of the segments were detected and twelve Mel-frequency cepstral coefficients were extracted as feature parameters which were then put into Back-Propagation Network as training and test samples. The numbers of training samples were set to 20, 40, 60, 80 and 100, and the number of test samples of the four beetle species were 54, 95, 54 and 50 separately. The results indicated that the identification success increased with the rise of the number of training samples. As the number of training samples increased to 100, the identification success reached the highest level, 98.14%, averaging 93.29%. In order to verify how the number of beetle species affects to recognition rates, the four species were subjected to pairwise comparison and the results showed that the recognition rates were higher than that when four species were tested together. |
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| Keywords: | bark beetle acoustic automatic classification BP Network MFCC |
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