摘要
为了解决传统方法识别声音信息异常点时存在精确度低的问题,研究基于声源定位的信息传输异常点智能识别算法,采用改进模糊C均值聚类算法得到可能性C均值聚类算法,采用此声源定位算法计算异常声源聚类中心,当聚类符合限制条件时,输出的聚类中心为异常声源定位结果;以该结果为前提,依据短时幅度与短时过动态门限率变量判断声音信息异常点的起始端与终止端,识别出声音信息传输异常点。实验结果表明,所提算法对识别声音信息传输异常点的丢包率误差最大在3.45~3.7之间,说明所提算法对丢包率存在一定的抵抗能力。
An information transmission abnormal point intelligent identification algorithm based on sound source localization is researched to solve the low accuracy problem existing during identification of sound information abnormal points by using traditional methods. The improved fuzzy C-means clustering algorithm is used to obtain the possibility C-means clustering algorithm. The sound source localization algorithm is adopted to calculate the clustering centers of abnormal sound sources. When the clustering meets the restriction conditions,the output clustering centers are considered as the localization results of abnormal sound sources. Taking the results as the prerequisites,the start terminal and end terminal of sound information abnormal points are judged according to the short-term amplitude and short-term over-dynamic threshold rate variables,so as to identify the abnormal points of sound information transmission. The experimental results show that the proposed algorithm′ s maximum packet loss rate error for identification of sound information transmission abnormal points is between 3.45 and 3.7,which shows that the proposed algorithm has a certain resistance to packet loss rate.
作者
柳秀山
蔡君
张琴
程骏
LIU Xiushan;CAI Jun;ZHANG Qin;CHENG Jun(Guangdong Polytechnic Normal University,Guangzhou 510665,China)
出处
《现代电子技术》
北大核心
2019年第12期33-36,共4页
Modern Electronics Technique
基金
国家自然科学基金(61571141)
广东省科技厅科技发展专项资金(2017A090905023)~~
关键词
声源定位
可能性C均值
聚类算法
信息传输
异常点识别
智能识别算法
sound source localization
possibility C means
clustering algorithm
information transmission
abnormal point identification
intelligent identification algorithm
