[1]蒋翠清,邵宏波. 基于MFCC与改进ACF的汽车声音识别算法研究[J].计算机技术与发展,2015,25(02):140-143.
　JIANG Cui-qing,SHAO Hong-bo. Research on Vehicle Audio Recognition Algorithm Based on MFCC and Improved ACF[J].,2015,25(02):140-143.
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 基于MFCC与改进ACF的汽车声音识别算法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年02期
页码:
140-143
栏目:
安全与防范
出版日期:
2015-02-10

文章信息/Info

Title:
 Research on Vehicle Audio Recognition Algorithm Based on MFCC and Improved ACF
文章编号:
1673-629X（2015)02-0140-04
作者:
 蒋翠清; 邵宏波
 合肥工业大学 管理学院
Author(s):
 JIANG Cui-qing; SHAO Hong-bo
关键词:
 ; 汽车声音识别; 梅尔倒谱系数; 自相关函数; 高斯混合模型
Keywords:
 ; vehicle audio recognition; MFCC; ACF; Gaussian mixture model
分类号:
TP18
文献标志码:
A
摘要:
 汽车声音识别是汽车声源定位等研究的基础，对交通事故鉴定、犯罪举证和犯罪现场还原等具有重要意义。现有汽车声音识别算法存在算法复杂度高和识别率相对较低等问题。针对现行问题，将以梅尔倒谱系数( MFCC)特征与自相关函数(ACF)方差作为混合特征的汽车声音识别算法应用到汽车声音识别系统中。该算法使用高斯混合模型(GMM)进行汽车声音建模和识别，获得比MFCC特征及其一阶差分特征组成的混合特征更好的识别效果。并通过仿真实验证明了该算法的有效性。
Abstract:
 Vehicle audio recognition is the foundation of vehicle sound source localization and other automotive research,it is very impor-tant for traffic accidents identification,crime scene evidence and crime reduction. The problem of high computational complexity and rela-tively low recognition rate has existed in current vehicle audio recognition. Concerning those problems above,the vehicle recognition algo-rithm taking Mel-Frequency Cepstrum Coefficients and improved Auto-Correlation Function as hybrid feature is applied in the vehicle audio recognition system. Modeling and classifying by the Gaussian Mixture Model,this feature vector outperforms MFCC and Differenti-al MFCC features in recognition. The simulation results prove the effectiveness of the proposed algorithm.

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更新日期/Last Update: 2015-04-28