现代制造工程 ›› 2018, Vol. 454 ›› Issue (7): 58-64.doi: 10.16731/j.cnki.1671-3133.2018.07.011

• 车辆工程制造技术 • 上一篇    下一篇

汽车座椅记忆盒技术性能自动检测研究

赵渊, 孙玉坤, 张宝昆, 张文龙, 张业宏   

  1. 燕山大学机械工程学院,秦皇岛 066004
  • 收稿日期:2017-03-10 出版日期:2018-07-20 发布日期:2018-07-20
  • 通讯作者: 孙玉坤,通信作者,硕士研究生,主要研究领域为机电系统控制及自动化。E-mial:sykzyp@163.com;1506446003@qq.com
  • 作者简介:赵渊,硕士研究生,主要研究领域为机电系统控制及自动化。

Research of automatic detection for car seat memory box technical performance

Zhao Yuan, Sun Yukun, Zhang Baokun, Zhang Wenlong, Zhang Yehong   

  1. School of Mechanical Engineering,Yanshan University,Qinhuangdao 066004,Hebei,China
  • Received:2017-03-10 Online:2018-07-20 Published:2018-07-20

摘要: 汽车电动座椅记忆盒出厂前通过人工识别蜂鸣器发出的声音间隔判断其工作状态,由于外部环境干扰,人耳识别经常发生错判漏判的现象。针对这一问题提出通过计算机对座椅蜂鸣器发出声音进行采集,采用动态可变窗长结合双门限对声音进行端点检测。短时能量结合MFCC系数作为声音混合特征参数进行提取,由改进的DTW算法进行模板匹配。实验结果表明,该系统可以准确识别目标声音,且在噪声条件下具有很好的鲁棒性。

关键词: 汽车座椅记忆盒, 声音识别, 自动检测, 改进端点检测, 改进动态时间规整

Abstract: The automobile electric seat memory box need to determine the status of the work by the sound interval of the buzzer before they leave the factory,ear recognition often misjudge the missing problem due to the interference of external environment.In order to solve this problem,the computer is used to collect the sound of the buzzer,and voice is detected by using dynamic variable window length with double threshold endpoint.The short-time energy with MFCC coefficient as the sound characteristic parameters are extracted into the improved DTW algorithm and template matching.Experimental results show that the system can identify the target sound and have good robustness under the condition of noise.

Key words: car seat memory box, sound identification, automatic detection, improved endpoint detection, improved dynamic time warping

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