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| 交通事件小波分解与支持向量机声频识别方法 | |
| 引用本文： | 罗向龙, 高静怀, 牛国宏, 潘若禹. 交通事件小波分解与支持向量机声频识别方法[J]. 交通运输工程学报, 2010, 10(2): 116-121. doi: 10.19818/j.cnki.1671-1637.2010.02.021 |
| 作者姓名： | 罗向龙 高静怀 牛国宏 潘若禹 |
| 作者单位： | 1.长安大学 信息工程学院, 陕西 西安 710064;2.西安交通大学 电子与信息工程学院, 陕西 西安 710049;3.西安市政设计研究院有限公司, 陕西 西安 710068;4.西安邮电学院 通信与信息工程学院, 陕西 西安 710064 |
| 基金项目： | 陕西省自然科学基金项目(SJ08-ZT13-2,2009JM8011);;河南省交通科技项目(2009P245) |
| 摘 要： |  分析了现有交通事件自动检测和识别方法, 提出了应用小波分解与支持向量机相结合的交通事件声频识别方法。将车辆行驶的声音信号进行小波分解, 以不同频段的重构信号能量作为特征向量, 对由多个支持向量机构成的交通事件分类器进行训练, 并对正常行驶、刹车和碰撞事件的声音信号进行识别。试验结果表明: 利用车辆声音信号能够正确识别不同的交通事件, 识别准确率达95%, 识别方法可行。 |
| 关 键 词： | 交通信息处理 交通事件 小波分解 支持向量机 |
| 收稿时间： | 2009-12-12 |
Traffic incident acoustic recognition method based on wavelet decomposition and support vector machine |
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| LUO Xiang-long, GAO Jing-huai, NIU Guo-hong, PAN Ruo-yu. Traffic incident acoustic recognition method based on wavelet decomposition and support vector machine[J]. Journal of Traffic and Transportation Engineering, 2010, 10(2): 116-121. doi: 10.19818/j.cnki.1671-1637.2010.02.021 | |
| Authors: | LUO Xiang-long GAO Jing-huai NIU Guo-hong PAN Ruo-yu |
| Affiliation: | 1. School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China;2. School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China;3. Xi'an Municipal Engineering Design and Research Institute Co., Ltd., Xi'an 710068, Shaanxi, China;4. School of Communication and information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710064, Shaanxi, China |
| Abstract: | The existing automatic detection and recognition methods of traffic incidents were analyzed,a recognition method with vehicle acoustic signals was proposed based on wavelet decomposition(WD) and support vector machine(SVM).Vehicle acoustic signals were decomposed with WD,the powers in different frequencies were regarded as different incident eigenvectors,and the traffic incident classifier composed of multiple SVMs was trained.The acoustic signals of normal driving,braking and crash incidents were recognize... |
| Keywords: | traffic information processing traffic incident wavelet decomposition SVM |
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