Speech/pause detection algorithm based on the adaptive method of complementary decomposition and energy assessment of intrinsic mode functions

Alimuradov, Alan K.; Tychkov, Alexander Yu.; Ageykin, Alexey V.; Churakov, Pyotr P.; Kvitka, Yury S.; Zaretskiy, A. P.

doi:10.1109/scm.2017.7970665

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…In view of such problems, Yeh et al [1] proposed the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method which reduces the reconstruction errors by adding white noise with opposite signs into the original signals for multiple times. Compared with the applications of Ensemble Empirical Mode Decomposition (EEMD) [2-4], the CEEMD method has a wider application range, such as in fields of instrument failure detection [5][6][7][8], financial law prediction [9][10][11][12][13], and environment model amendments [14,15], etc. However, due to the addition of paired white noise, the amount of calculation will increase and the calculation efficiency will decrease.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Filtering Method for Bridge Vibration Signals Based on Improved CEEMDAN and Multi-Scale Permutation Entropy

He¹,

Wang²,

Gao³

et al. 2021

EESRJ

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Aiming at the serious noise of bridge vibration signals in complex environment, this paper proposed an adaptive filtering and denoising optimization method for bridge structural health monitoring. The method took CEEMDAN algorithm as the core, during the step-by-step decomposition of original signals, white noise with opposite signs was added in each stage, meanwhile multi-scale permutation entropy (MPE) was introduced to analyze the mean entropy of each intrinsic mode function (IMF) at different scales, and components with serious noise were eliminated to complete the first filtering; then, in order to optimize the remaining IMFs for signal reconstruction and ensuring the smoothness and similarity of filtering, an optimized reconstruction model was established to complete the second filtering. Compared with the CEEMDAN method, the proposed method could solve the problems of mode mixing and endpoint effect with good completeness, orthogonality, and signal-to-noise ratio. At last, the advantages and application value of the proposed method were verified again by the vibration signal analysis of a real long-span bridge structure.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Filtering Method for Bridge Vibration Signals Based on Improved CEEMDAN and Multi-Scale Permutation Entropy

He¹,

Wang²,

Gao³

et al. 2021

EESRJ

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show abstract

“…In this paper, the authors propose a method for increasing the detection efficiency of borderline mental disorders based on adaptive decomposition technology for non-stationary signals, namely, improved complete ensemble empirical mode decomposition with adaptive noise and mel-frequency cepstral analysis. The study is a development of previously published works of the authors [8,9].…”

Section: Introductionmentioning

confidence: 99%

Increasing detection efficiency of border mental disorders based on adaptive decomposition and cepstral analysis of speech signals.

Alimuradov¹,

Tychkov²,

Churako³

et al. 2019

JRE

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The detection accuracy of borderline mental disorders depends on correct processing of speech signals. The main reason of low accuracy and large errors in measurements is associated with the use of inefficient and non-adaptive methods for processing of non-stationary speech signals. In this paper, the authors propose a method for increasing the detection efficiency of borderline mental disorders based on adaptive decomposition technology for non-stationary signals, namely, improved complete ensemble empirical mode decomposition with adaptive noise and melfrequency cepstral analysis. A block diagram for the method and a brief mathematical description are presented. The research results are presented, on the basis of which it was concluded that the method proposed by the authors can successfully be tested in remote monitoring systems of psychogenic disorders to accelerate the treatment process.

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“…Currently,variousexperimentalandstatisticaltechniquesanddifferentiationofsignalprocessing methodsonaccessiblerecordingchannelsofthehumanbodyreactionsareusedforthedetection of borderline mental disorders. Methods for evaluation, implemented on the basis of video data reflecting mimic and gestural changes (Alimuradov, Tychkov, Ageykin, Churakov, Kvitka, & Zaretskiy, 2017;Alimuradov, 2017); signals reflecting parameters of physiological activity of a human body (electroencephalography, electrocardiography, electromyography, etc.) (Alimuradov, Tychkov, Frantsuzov, & Churakov, 2015;Agrafioti, 2011;Barabanschikov & Zhegallo, 2014); biochemicalbloodparameters (Bobkov,2013;Camacho,&Harris,2008);parametersofhandwriting andkeyboardwritingoftexts (Cheveigne,&Kawahara,2002;Colominasa,Schlotthauera,&Torres, 2014);parametersofoculography(eyetracking) (Darley,Aronson,&Brown,1969;Davydov,Kiselev, Kochetkov,&Tkachenya,2011)areofparticularinterest.…”

mentioning

confidence: 99%

Improved CEEMDAN Based Speech Signal Analysis Algorithm for Mental Disorders Diagnostic System

Alimuradov

Tychkov

Kuzmin

et al. 2019

International Journal of Embedded and Real-Time Communication Systems

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An automated algorithm for pitch frequency measurement for diagnostic systems of borderline mental disorders is developed. It is based on decomposition of a speech signal into frequency components using an adaptive method for analyzing of non-stationary signals, improved complete ensemble empirical mode decomposition with adaptive noise (improved CEEMDAN), and isolating the component containing pitch. A block diagram for the developed algorithm and a detailed mathematical description are presented. A research of the algorithm using the formed verified signal base of healthy patients, and male and female patients with psychogenic disorders, aged from 18 to 60, is conducted. The research results are evaluated in comparison with the known algorithms for pitch frequency measurement. In accordance with the results of the study, the developed algorithm for pitch frequency measurement provides an accuracy increase in determination of borderline mental disorders: for the error of the first kind, on the average, it is more accurate by 10.7%, and for the second type error by 4.7%.

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Speech/pause detection algorithm based on the adaptive method of complementary decomposition and energy assessment of intrinsic mode functions

Cited by 7 publications

References 6 publications

An Adaptive Filtering Method for Bridge Vibration Signals Based on Improved CEEMDAN and Multi-Scale Permutation Entropy

An Adaptive Filtering Method for Bridge Vibration Signals Based on Improved CEEMDAN and Multi-Scale Permutation Entropy

Increasing detection efficiency of border mental disorders based on adaptive decomposition and cepstral analysis of speech signals.

Improved CEEMDAN Based Speech Signal Analysis Algorithm for Mental Disorders Diagnostic System

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