标题:A signal period detection algorithm based on morphological self-complementary Top-Hat transform and AMDF
作者:Han, Zhao ;Wang, Xiaoli
作者机构:[Han, Zhao ;Wang, Xiaoli ] School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai; 264209, China
通讯作者:Wang, Xiaoli
来源:Information (Switzerland)
出版年:2019
卷:10
期:1
DOI:10.3390/info10010024
关键词:Adaptive threshold; Average magnitude difference function; Period detection; Self-complementary Top-Hat transform; Single period; Weak characteristic signal
摘要:Period detection technology for weak characteristic signals is very important in the fields of speech signal processing, mechanical engineering, etc. Average magnitude difference function (AMDF) is a widely used method to extract the period of periodic signal for its low computational complexity and high accuracy. However, this method has low detection accuracy when the background noise is strong. In order to improve this method, this paper proposes a new method of period detection of the signal with single period based on the morphological self-complementary Top-Hat (STH) transform and AMDF. Firstly, the signal is de-noised by the morphological self-complementary Top-Hat transform. Secondly, the average magnitude difference function of the noise reduction sequence is calculated, and the falling trend is suppressed. Finally, a calculating adaptive threshold is used to extract the peaks at the position equal to the period of periodic signal. The experimental results show that the accuracy of periodic extraction of AMDF after Top-Hat filtering is better than that of AMDF directly. In summary, the proposed method is reliable and stable for detecting the periodic signal with weak characteristics. © 2019 by the authors.
收录类别:EI;SCOPUS
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059988904&doi=10.3390%2finfo10010024&partnerID=40&md5=260d1056b48117b27de189be3099fedc
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