Analysis and Classification of Cold Speech Using Variational Mode Decomposition

Deb, Suman; Dandapat, Samarendra; Krajewski, Jarek

doi:10.1109/taffc.2017.2761750

Cited by 47 publications

(23 citation statements)

References 49 publications

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“…Moreover, the sum of these functions exactly gives the original signal. The detailed formulation and the algorithm are given in [12,13]. The VMD is briefly introduced as follows:…”

Section: Variational Mode Decomposition (Vmd) Algorithmmentioning

confidence: 99%

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Pattern recognition of unknown partial discharge based on improved SVDD

Gao

Zhu

Jia

2018

IET Science, Measurement & Technology

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“…Moreover, the sum of these functions exactly gives the original signal. The detailed formulation and the algorithm are given in [12,13]. The VMD is briefly introduced as follows:…”

Section: Variational Mode Decomposition (Vmd) Algorithmmentioning

confidence: 99%

“…Then the alternate direction method of multipliers (ADMM) is employed to solve (2) according to [13]…”

Section: Variational Mode Decomposition (Vmd) Algorithmmentioning

confidence: 99%

Pattern recognition of unknown partial discharge based on improved SVDD

Gao

Zhu

Jia

2018

IET Science, Measurement & Technology

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“…The intention of this paper was to develop a simple classi-fication system with increased efficiency for separating the healthy, and seizure free segments. Deb et al [16] utilized a variational Mode Decomposition (VMD) technique for analyzing and classifying the cold speech. In this system, the speech signal was decomposed into varying number of modes or sub-signals for characterization of crisp speech.…”

Section: Related Workmentioning

confidence: 99%

“…This tool is also used to know the cause of unconsciousness in comatose patients [3]. Spectral information of EEG signal can be obtained by focusing on the frequency bands, namely, Alpha waves (8)(9)(10)(11)(12), Beta waves (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), Gamma waves (above 30 Hz), Theta waves (4)(5)(6)(7)(8) and Delta waves (1)(2)(3)(4). These enable easy understanding for an accurate diagnosis of the classifications as mentioned above, signifing a different mental state of a patient.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Prediction Model for Automated Brain Abnormalities Detection

Anand

Jaiswal

Ghosh

2018

CSENG

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Background: The rapid improvement in technology enables an Electroencephalogram (EEG) to detect a diverse range of brain disorders easily. The design of sophisticated signal processing methods for an efficient analysis of the EEG signals is exceptionally essential. Raw EEG signal is contaminated by noise and artefacts that modify the spectral-spatial and temporal information of the signal and renders inaccurate clinical interpretation. Denoising of the signal is the first step to refine the signal quality and identify patient's mental state from the signal although it is not an easy task because of high dimensionality and complexity of EEG signal. The present study highlights three conditions of the brain namely stroke, brain death, and a healthy state. The primary concern is to detect the most abnormal conditions of the brain, i.e., an EEG with a critical stage.Method: This paper introduces a neoteric technique for the analysis of EEG signals of the three conditions using filters such as Fuzzy filter and wavelet orthogonal filter to obtain highly accurate resultant signals. Further, the resultant filter is trained in Neural Network for predicting the brain abnormalities. The proposed system is found to be efficient in denoising the EEG waves. Results:The result shows that the classification accuracy of multiclass EEG dataset achieved and the performance of ANN is high and it was found to be the best validation performance of ANN which is 0.2303. Conclusion:This paper comprehensively describes the denoising of the EEG signals that will provide accuracy in the diagnosis of the EEG to detect brain disorders. The Fuzzy filter pre-processes the signals by considering the noisy signal by an ideal value in such a way that the desired metric (the filtered output) is reduced. The orthogonal wavelet filter produces a single scaling function and wavelet function. The EEG features are extracted from multiple-level decompositions of EEGs by DWT. Finally, the features are classified using Back propagation artificial neural network that categorizes the EEGs to make the diagnosis easier for the brain abnormalities.

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“…It is also a kind of T‐F signal analysis tool which is excellent in the processing of nonlinear and nonstationary signals. It is being used widely in many areas like image processing, 30 fault detection, 31‐33 forecasting, 34 speech processing, 35 and signal denoising 36,37 . Earlier, various decomposition methods like Empirical Mode Decomposition (EMD), Local Mean Decomposition (LMD) were used extensively in various fields but the problem of mode mixing has restricted their application.…”

Section: Introductionmentioning

confidence: 99%

An efficient interference mitigation approach for NavIC receivers using improved variational mode decomposition and wavelet packet decomposition

Lorraine

Madhu

2021

Trans Emerging Tel Tech

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Reliable positioning, timing, and navigation services have become vitally important in safety and security applications. Hence, the need for Global Navigation Satellite Systems (GNSS) is growing continually. However, Continuous Wave Interference (CWI) was found to be one of the major potential threats of GNSS systems which degrades the receiver's performance. So, in this paper, a new approach using Improved Variational Mode Decomposition and Wavelet Packet Decomposition (IVMD‐WPD) has been proposed to mitigate CWI in NAVigation with Indian Constellation (NavIC) receivers. Although VMD is considered as an excellent signal analysis tool in decomposing non‐stationary and complex signals, the accuracy of the decomposition results depends upon the parameter setting. To address this, firstly, Normalized Kurtosis Energy Ratio (nKER) evaluation index is constructed. Then, the principle of nKER maximum is implemented to find the optimal parameters of VMD. Using the optimized parameters, the received signal is decomposed by IVMD into sub‐signals. Secondly, the mutual information index is introduced to extract the information dominant modes. The extracted modes are then processed by wavelet packet filter and finally, the desired signal is reconstructed. The proposed IVMD‐WPD method not only reduces the jamming efficiently but also overcomes the limitations of VMD. Moreover, by integrating IVMD with wavelet packet filter, the remaining effects of jamming and noise present in desired modes can be filtered thereby enhancing the performance. Simulation results reveal that the proposed method performs better in comparison with the conventional techniques in case of a single tone, multi‐tone, and chirp jamming environments.

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Analysis and Classification of Cold Speech Using Variational Mode Decomposition

Cited by 47 publications

References 49 publications

Pattern recognition of unknown partial discharge based on improved SVDD

Pattern recognition of unknown partial discharge based on improved SVDD

A Machine Learning Prediction Model for Automated Brain Abnormalities Detection

An efficient interference mitigation approach for NavIC receivers using improved variational mode decomposition and wavelet packet decomposition

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