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Information 2019, 10(1), 24; https://doi.org/10.3390/info10010024

A Signal Period Detection Algorithm Based on Morphological Self-Complementary Top-Hat Transform and AMDF

School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
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Received: 14 November 2018 / Revised: 2 January 2019 / Accepted: 7 January 2019 / Published: 11 January 2019
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Abstract

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. View Full-Text
Keywords: weak characteristic signal; period detection; single period; self-complementary Top-Hat transform; average magnitude difference function; adaptive threshold weak characteristic signal; period detection; single period; self-complementary Top-Hat transform; average magnitude difference function; adaptive threshold
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Han, Z.; Wang, X. A Signal Period Detection Algorithm Based on Morphological Self-Complementary Top-Hat Transform and AMDF. Information 2019, 10, 24.

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