A review on intrensic mode function of EMD

Chaudhari, Harshal A.; Nalbalwar, Sanjay L.; Sheth, Rashmee

doi:10.1109/iceeot.2016.7755114

Cited by 19 publications

(14 citation statements)

References 9 publications

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“…Thus the reason behind the proposed method employs a fair noise estimating method called NLM estimation for de‐noising the noisy speech signal. The performance of the suggested method depicted below at the graphical representation compared with other existing methods such as EMD and variation mode decomposition (EMD–VMD), VMD–NLM and least mean square (LMS) adaptive filtering methods [28].…”

Section: Resultsmentioning

confidence: 99%

“…A sifting process [28] is involved in the signal decomposition

y ()(, r)

in several IMFs. It is described as given below.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The first component

c_{1} ()(, r)

is computed through subtracting the mean

m_{1} ()(, r)

from the original speech signal

y ()(, r)

that is expressed as [28]

c_{1} ()(, r) = y ()(, r) - m_{1} ()(, r)

c_{1} ()(, r)

meets the IMF conditions mentioned above, it is assumed as the first IMF

I_{1} ()(, r)

.If

c_{1} ()(, r)

has unsatisfied IMF conditions,

y ()(, r)

is migrated through

c_{1} ()(, r)

as well as steps 1, 2, 3 and 4 are repeated [28] to derive (11)

c_{2} ()(, r) = c_{1} ()(, r) - m_{2} ()(, r)

where

m_{2} ()(, r)

represents the novel mean computed by migrating

y ()(, r)

along

c_{1} ()(, r)

by using steps 1, 2 and 3.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Also if

c_{2} ()(, r)

does not meet the conditions of the IMFs, then SD is computed utilising the two continuous results from the sifting process. Thus, the SD to sifting process [28] is expressed as

normalSD = \sum_{r = 0}^{N} \frac{{|c_{j - 1} (r) - c_{j} (r)|}^{2}}{c_{j - 1}^{2} (r)}

where SD is referred to as stopping criteria based on sifting process, thus the sifting process is stopped to predefine SD values between 0.2 and 0.3 and thus

c_{j} ()(, r)

is considered as first IMF

I_{1} ()(, r)

.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…After obtaining

I_{1} ()(, r)

, identify the variation among

I_{1} ()(, r)

, the signal of original

y ()(, r)

to obtain the residual signal

q_{1} ()(, r)

[28]. Thus,

q_{1} ()(, r)

is given as

q_{1} ()(, r) = y ()(, r) - I_{1} ()(, r)

Later, the sifting process is used on the new residual signal,

q_{1} ()(, r)

, to obtain the following IMF as well as the residual signal

q_{2} ()(, r)

.…”

Section: Proposed Methodologymentioning

confidence: 99%

See 4 more Smart Citations

Real‐time speech enhancement using optimised empirical mode decomposition and non‐local means estimation

Vumanthala¹,

Bikshalu²

2020

IET Computers & Digital Techniques

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Section: Resultsmentioning

confidence: 99%

“…A sifting process [28] is involved in the signal decomposition

y ()(, r)

in several IMFs. It is described as given below.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The first component

c_{1} ()(, r)

is computed through subtracting the mean

m_{1} ()(, r)

from the original speech signal

y ()(, r)

that is expressed as [28]

c_{1} ()(, r) = y ()(, r) - m_{1} ()(, r)

c_{1} ()(, r)

meets the IMF conditions mentioned above, it is assumed as the first IMF

I_{1} ()(, r)

.If

c_{1} ()(, r)

has unsatisfied IMF conditions,

y ()(, r)

is migrated through

c_{1} ()(, r)

as well as steps 1, 2, 3 and 4 are repeated [28] to derive (11)

c_{2} ()(, r) = c_{1} ()(, r) - m_{2} ()(, r)

where

m_{2} ()(, r)

represents the novel mean computed by migrating

y ()(, r)

along

c_{1} ()(, r)

by using steps 1, 2 and 3.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Also if

c_{2} ()(, r)

does not meet the conditions of the IMFs, then SD is computed utilising the two continuous results from the sifting process. Thus, the SD to sifting process [28] is expressed as

normalSD = \sum_{r = 0}^{N} \frac{{|c_{j - 1} (r) - c_{j} (r)|}^{2}}{c_{j - 1}^{2} (r)}

where SD is referred to as stopping criteria based on sifting process, thus the sifting process is stopped to predefine SD values between 0.2 and 0.3 and thus

c_{j} ()(, r)

is considered as first IMF

I_{1} ()(, r)

.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…After obtaining

I_{1} ()(, r)

, identify the variation among

I_{1} ()(, r)

, the signal of original

y ()(, r)

to obtain the residual signal

q_{1} ()(, r)

[28]. Thus,

q_{1} ()(, r)

is given as

q_{1} ()(, r) = y ()(, r) - I_{1} ()(, r)

Later, the sifting process is used on the new residual signal,

q_{1} ()(, r)

, to obtain the following IMF as well as the residual signal

q_{2} ()(, r)

.…”

Section: Proposed Methodologymentioning

confidence: 99%

See 3 more Smart Citations

Real‐time speech enhancement using optimised empirical mode decomposition and non‐local means estimation

Vumanthala¹,

Bikshalu²

2020

IET Computers & Digital Techniques

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Resolving mode mixing in boiling water reactors instability analysis using variational mode decomposition

Montalvo

Gavilan

Garcı́a-Berrocal

et al. 2020

Intl J of Energy Research

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In dynamic, complex and nonlinear systems, the analysis of transients, instabilities or unusual events is fundamental, as well as the knowledge of the system's behavior in the frequency space. What is usual in this type of study is to have discrete and digitally acquired data, due to a sampling process with a determined period or frequency. Extracting information from this data set is done by numerical methods, such as the Fourier Transform (FFT), the Hilbert-Huang Transform (HHT) or techniques from chaos mathematics (Attractors). In this way we have scarce data, with some loss of information, analyzed with numerical techniques that introduce, also, some dilution of the information. In particular, the techniques of analysis in the frequency space, by their very nature, blur, frequencies and bandwidths. This leads in some cases to the identification of unsuitable or very wide frequencies or intervals, which make the extracted information unusable. To this effect, we have studied the VMD technique that provides more real and representative frequencies of the system as well as narrower bandwidths that make possible better identification of phenomena and influences. This technique has been applied to the analysis of thermohydraulic instabilities in nuclear boiling water reactors (BWR). The cases had already been analyzed with STFT and HHT techniques. The results of the application of the VMD shows that the mode mixing is solved, so modes obtained are monofrequency and these frequency bands are narrower than in previous methodologies. This improved performance allows using backbone plots for identifying which modes are excited in instabilities events.

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A Novel FD3 Framework for Carbon Emissions Prediction

Liu

et al. 2023

Environ Model Assess

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A review on intrensic mode function of EMD

Cited by 19 publications

References 9 publications

Real‐time speech enhancement using optimised empirical mode decomposition and non‐local means estimation

Real‐time speech enhancement using optimised empirical mode decomposition and non‐local means estimation

Resolving mode mixing in boiling water reactors instability analysis using variational mode decomposition

A Novel FD3 Framework for Carbon Emissions Prediction

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