摘要
提出一种基于变步长最小均方(LMS)和支持向量机(SVM)的电能表内异物声音自动识别方法。由于SVM分类器对噪声敏感,通过变步长LMS实现对采集的电能表内异物声音信号的降噪,相较于固定步长LMS,信噪比提升明显,耗用时间较少。对声音信号进行时、频域和倒谱分析,并提取其短时特征系数及改进梅尔频率倒谱系数(MFCC)。并采用短时能量和MFCC系数构成混合特征矩阵,对该矩阵降维后输入SVM进行异物声音识别。实验证明:提出的方法计算量小、识别率高,有很好的应用价值。
An automatic identification method of foreign object sound in electric energy meter based on variable step size least mean square(LMS)and support vector machine(SVM)is proposed.In consideration of the fact that the SVM classifier is sensitive to noise,noise reduction of the foreign object sound signal in the collected electric energy meter is realized by using the variable step size LMS.Compared with the fixed step length LMS,the signal to noise ratio is improved significantly and the time consumption is reduced.The sound signal is analyzed in time,frequency and cepstrum domain,and its short-term characteristic coefficient and improved Meier frequency cepstrum coefficient(MFCC)coefficient is extracted.The short-time energy and MFCC coefficients are used to form mixed feature matrix,which is input a SVM for foreign object sound recognition after dimension reduction.The experimental results indicate that the method has small computational amount ,high recognition rate,and has good application value.
作者
蒋晓永
杨涛
JIANG Xiao-yong;YANG Tao(School of Information Engineering,Southwest University of Science and Technology,Mianyang 621000,China;Key Laboratory of Sichuan Province for Robot Technology Used for Special Environment,Mianyang 621000,China)
出处
《传感器与微系统》
CSCD
2019年第2期143-146,共4页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(61571376)
特殊环境机器人技术四川省重点实验室开放资助项目(13ZXTK06)
关键词
电能表异物声音
变步长最小均方
短时能量
改进梅尔频率例谱系数
支持向量机识别
electric energy meter foreign object sound
variable step size least mean square(LMS)
short-time energy
improved Meier frequency cepstrum coefficient (MFCC) coefficient
support vector machine ( SVM)identification
