Epoch Estimation from Emotional Speech Signals Using Variational Mode Decomposition

Lal, G. Jyothish; Gopalakrishnan, E. A.; Govind, D.

doi:10.1007/s00034-018-0804-x

Cited by 28 publications

(2 citation statements)

References 46 publications

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“…Among them, in recent years, multivariate empirical mode decomposition (MEMD) (Zheng and Xu, 2019), noise-assisted MEMD (NAMEMD) (Ahrabian et al, 2012), variational mode decomposition (VMD) (Dragomiretskiy and Zosso, 2014), successive variational mode decomposition (SVMD) (Nazari and Sakhaei, 2020), empirical wavelet transform (EWT) (Gilles, 2013;Hurat, 2020), UPEMD (Wang et al, 2018), improved UPEMD (IUPEMD) (Hurat, 2020;Ying et al, 2021;Zheng et al, 2021) have been proposed successively. The effectiveness of the above methods has been verified in mechanical fault detection, voice signal, ECG signal, and seismic signal processing (Lal et al, 2018;Zeng and Yuan, 2021;Liu et al, 2022). In order to study the influence of different EMD-based solutions on the accuracy and reliability of ECGI inverse operation, firstly, the ECGs were decomposed using the various EMD-based solutions mentioned above in this paper.…”

Section: Introductionmentioning

confidence: 99%

Solving the inverse problem in electrocardiography imaging for atrial fibrillation using various time-frequency decomposition techniques based on empirical mode decomposition: A comparative study

Zhang

Lian

2022

Front. Physiol.

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Electrocardiographic imaging (ECGI) can aid in identifying the driving sources that cause and sustain atrial fibrillation (AF). Traditional regularization strategies for addressing the ECGI inverse problem are not currently concerned about the multi-scale analysis of the inverse problem, and these techniques are not clinically reliable. We have previously investigated the solution based on uniform phase mode decomposition (UPEMD-based) to the ECGI inverse problem. Numerous other methods for the time-frequency analysis derived from empirical mode decomposition (EMD-based) have not been applied to the inverse problem in ECGI. By applying many EMD-based solutions to the ECGI inverse problem and evaluating the performance of these solutions, we hope to find a more efficient EMD-based solution to the ECGI inverse problem. In this study, five AF simulation datasets and two real datasets from AF patients derived from a clinical ablation procedure are employed to evaluate the operating efficiency of several EMD-based solutions. The Pearson’s correlation coefficient (CC), the relative difference measurement star (RDMS) of the computed epicardial dominant frequency (DF) map and driver probability (DP) map, and the distance (Dis) between the estimated and referenced most probable driving sources are used to evaluate the application of various EMD-based solutions in ECGI. The results show that for DF maps on all simulation datasets, the CC of UPEMD-based and improved UPEMD (IUPEMD)-based techniques are both greater than 0.95 and the CC of the empirical wavelet transform (EWT)-based solution is greater than 0.889, and the RDMS of UPEMD-based and IUPEMD-based approaches is less than 0.3 overall and the RDMS of EWT-based method is less than 0.48, performing better than other EMD-based solutions; for DP maps, the CC of UPEMD-based and IUPEMD-based techniques are close to 0.5, the CC of EWT-based is 0.449, and the CC of the remaining EMD-based techniques on the SAF and CAF is all below 0.1; the RDMS of UPEMD-based and IUPEMD-based are 0.06∼0.9 less than that of other EMD-based methods for all the simulation datasets overall. On two authentic AF datasets, the Dis between the first 10 real and estimated maximum DF positions of UPEMD-based and EWT-based methods are 212∼1440 less than that of others, demonstrating these two EMD-based solutions are superior and are suggested for clinical application in solving the ECGI inverse problem. On all datasets, EWT-based algorithms deconstruct the signal in the shortest time (no more than 0.12s), followed by UPEMD-based solutions (less than 0.81s), showing that these two schemes are more efficient than others.

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Section: Introductionmentioning

confidence: 99%

Solving the inverse problem in electrocardiography imaging for atrial fibrillation using various time-frequency decomposition techniques based on empirical mode decomposition: A comparative study

Zhang

Lian

2022

Front. Physiol.

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“…The VMD approach has been applied in the fields of electrocardiogram image processing (Mert, 2016;Singh & Pradhan, 2018), bearing fault diagnosis (Ma et al, 2019;Yan & Jia, 2019), seismic signal-noise suppression (Y. Huang et al, 2018;Proano et al, 2018) and speech signal recognition (Lal et al, 2018;Yadav et al, 2019) in just 5 years since its introduction. This is because the VMD approach has good robustness to noise, at the same time, it has both a solid mathematical foundation and high computational efficiency.…”

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confidence: 99%

Comparative Research on Noise Reduction of Transient Electromagnetic Signals Based on Empirical Mode Decomposition and Variational Mode Decomposition

Wei

Feng

et al. 2021

Radio Science

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Transient electromagnetic (TEM) approach is widely used in hydrogeology surveys in various countries all over the world due to the advantages of little random interference, sensitive response to low resistivity, strong ability to penetrate high resistivity, large exploration depth, and convenient construction, which provides basic data of hydrogeology and available regional groundwater resources for the construction of national economy, the development of society and the construction and protection of ecological environment (Ahmed, 2014;Haroon et al., 2018). However, TEM detection is always disturbed by ambient noise during on-site implementation. Normally, random noise, power frequency noise and sferics noise are the main interferences in TEM detection (Munkholm & Auken, 1996). They cause TEM signals to be distorted, which in turn affects the further processing and interpretation of data, thus obtaining conclusions that are not in line with reality. This inevitably reduces the accuracy of the interpretation of geophysical results, and may even lead to accidents. Therefore, it is necessary to reduce noise of TEM signals.According to the characteristics of TEM signal with wide frequency broadband, weak late signal, nonstationarity and nonlinearity (Wang et al., 2011), several approaches have been proposed to denoise TEM signal with the purpose of obtaining a denoised TEM that facilitates easy and accurate. The proposed approaches include wavelet transform (

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Speech Emotion Recognition Using CNN-LSTM and Vision Transformer

Kumar¹,

Maharana²,

Krishnan³

et al. 2023

Lecture Notes in Networks and Systems

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Epoch Estimation from Emotional Speech Signals Using Variational Mode Decomposition

Cited by 28 publications

References 46 publications

Solving the inverse problem in electrocardiography imaging for atrial fibrillation using various time-frequency decomposition techniques based on empirical mode decomposition: A comparative study

Solving the inverse problem in electrocardiography imaging for atrial fibrillation using various time-frequency decomposition techniques based on empirical mode decomposition: A comparative study

Comparative Research on Noise Reduction of Transient Electromagnetic Signals Based on Empirical Mode Decomposition and Variational Mode Decomposition

Speech Emotion Recognition Using CNN-LSTM and Vision Transformer

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