scholarly journals Identifying a creak probability threshold for an irregular pitch period detection algorithm

2019 ◽  
Vol 145 (5) ◽  
pp. EL379-EL385 ◽  
Author(s):  
Olivia Murton ◽  
Stefanie Shattuck-Hufnagel ◽  
Jeung-Yoon Choi ◽  
Daryush D. Mehta
Keyword(s):  
Detection Algorithm ◽  
Pitch Period ◽  
Period Detection ◽  
Probability Threshold

Significant Points: Pitch Period Detection as a Problem of Segmentation

Phonetica ◽  
1982 ◽  
Vol 39 (4-5) ◽  
pp. 241-253
Author(s):  
Kurt Schäfer-Vincent
Keyword(s):  
Pitch Period ◽  
Period Detection

Pitch Period Detection and Chaining: Method and Evaluation

Phonetica ◽  
1983 ◽  
Vol 40 (3) ◽  
pp. 177-202 ◽  
Author(s):  
Kurt Schäfer-Vincent
Keyword(s):  
Pitch Period ◽  
Period Detection

Improved Algorithm for Pitch Detection and Harmonic Separation

2013 ◽  
Vol 333-335 ◽  
pp. 753-763
Author(s):  
Yi Zhao ◽  
Sheng Zhang ◽  
Xiao Kang Lin
Keyword(s):  
Noise Immunity ◽  
Harmonic Signal ◽  
Detection Algorithm ◽  
Mean Value ◽  
Pitch Detection ◽  
Time Varying ◽  
Separation Algorithm ◽  
Pitch Period ◽  
Transfer Path ◽  
Path Search

In this paper, we have proposed a new algorithm for pitch detection and an idea for harmonic separation based on pitch detection. Firstly, we have introduced the pitch algorithm. It is mainly consisted of five parts: mean value removal, extraction of alternative pitch periods, best pitch transfer path search, accurate pitch period search with time-varying filter and the search of fractional pitch period. Then we have brought in a harmonic separation algorithm based on the pitch detection. The pitch detection algorithm and harmonic separation algorithm proposed in this paper is mutually beneficialExperiments results show that the new pitch detection algorithm can achieve higher accuracy. And compared with some other algorithms, this approach owns a better noise immunity. The harmonic separation algorithm can separate each harmonic signal accurately.

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

Information ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 24 ◽  
Author(s):  
Zhao Han ◽  
Xiaoli Wang
Keyword(s):  
Detection Algorithm ◽  
Periodic Signal ◽  
Detection Accuracy ◽  
Speech Signal Processing ◽  
Difference Function ◽  
Detection Technology ◽  
Period Detection ◽  
Magnitude Difference ◽  
Better Than ◽  
Average Magnitude Difference Function

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.

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