文献类型：期刊文献

英文题名：Short-time and Spectrum Features of Noises Made by Vehicles for Recognition

作者：Ding, Kai Zhang, Shigong Zhang, Kesheng Lei, Zhen

第一作者：Ding, Kai

机构：[1] Science and Technology on Near-Surface Detection Laboratory, Wuxi, China; [2] Guizhou Institute of Technology, Guiyang, China

第一机构：Science and Technology on Near-Surface Detection Laboratory, Wuxi, China

年份：2020

起止页码：501-504

外文期刊名：2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2020

收录：EI(收录号：20210709906722)

语种：英文

外文关键词：Spectrum analysis - Spectroscopy - Pattern recognition - Engines

摘要：The recognition and classification of moving maneuvering targets play an important role in intelligent vehicle highway system. In this paper, based on short-time and spectrum technologies, some general used features of noises made by vehicles are analyzed. Short-time zero-crossing rate and short-time energy do not show obvious identifiable characteristics for recognition. While, spectrum features of noises made by different vehicles represent obvious characteristics. These features include amplitude frequency spectrum, power spectrum density, and short time amplitude frequency spectrum, Engine speed and vehicle weight can be estimated from the spectrum features. Besides, nonlinear idle engine noise features can also be used in vehicle recognition. With a good theory basis, the voice-print features, such as pitch and formant frequencies, are considered the best methods for recognition, but the two features do not really display the corresponding role. Feature extraction based on spectrum features can provide a powerful theoretical and experimental basis for the next pattern recognition. ? 2020 IEEE.