Accent extraction of emotional speech based on modified ensemble empirical mode decomposition

Shen, Zhiyuan; Wang, Qiang; Shen, Yi; Jin, Jing; Lin, Yi‐Hung

doi:10.1109/imtc.2010.5488210

Cited by 15 publications

(12 citation statements)

References 12 publications

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“…This can lead to complications due to mode mixing. Mode mixing is defined as an IMF that includes oscillations of dramatically disparate scales or a component of similar scale residing in different IMFs [17], and can also be due to the presence of a transient spectral component in the signal. An extension to the EMD algorithm was proposed in [16] which eliminates this mode mixing dilemma.…”

Section: Ensemble Empirical Mode Decomposition (Eemd)mentioning

confidence: 99%

The Use of Ensemble Empirical Mode Decomposition With Canonical Correlation Analysis as a Novel Artifact Removal Technique

Sweeney

McLoone

Ward

2013

IEEE Trans. Biomed. Eng.

253

209

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Abstract-Biosignal measurement and processing is increasingly being deployed in ambulatory situations particularly in connected health applications. Such an environment dramatically increases the likelihood of artifacts which can occlude features of interest and reduce the quality of information available in the signal. If multichannel recordings are available for a given signal source then there are currently a considerable range of methods which can suppress or in some cases remove the distorting effect of such artifacts. There are however considerably fewer techniques available if only a single channel measurement is available and yet single channel measurements are important where minimal instrumentation complexity is required. This paper describes a novel artifact removal technique for use in such a context. The technique known as ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) is capable of operating on single channel measurements. The EEMD technique is first used to decompose the single channel signal into a multi-dimensional signal. The CCA technique is then employed to isolate the artifact components from the underlying signal using second order statistics. The new technique is tested against the currently available Wavelet denoising and EEMD-ICA techniques using both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) data and is shown to produce significantly improved results.

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Section: Ensemble Empirical Mode Decomposition (Eemd)mentioning

confidence: 99%

The Use of Ensemble Empirical Mode Decomposition With Canonical Correlation Analysis as a Novel Artifact Removal Technique

Sweeney

McLoone

Ward

2013

IEEE Trans. Biomed. Eng.

253

209

View full text Add to dashboard Cite

show abstract

“…The major one is the appearance of mode mixing, which is different modes of oscillations coexisting in a single IMF due to signal intermittency [17]. As such, Huang et al [17] proposed a noise-assisted scheme, known as Ensemble EMD (EEMD), "which defines the true IMF components as the mean of an ensemble of trials, each consisting of the signal plus a white noise of finite amplitude" [19]. In other words, based on the ensemble number (N), white noises with the same amplitude are added N times to the original signal to generate N modified signals.…”

Section: Vibration Analysis Using Empirical Mode Decompositionmentioning

confidence: 99%

Indicative Fault Diagnosis of Wind Turbine Generator Bearings Using Tower Sound and Vibration

2017

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Abstract:The idea of indicative fault diagnosis based on measuring the wind turbine tower sound and vibration is presented. It had been reported by a wind farm operator that a major fault on the generator bearing causes shock and noise to be heard from the bottom of the wind turbine tower. The work in this paper was conceived to test whether tower top faults could be identified by taking simple measurements at the tower base. Two accelerometers were attached inside the wind turbine tower, and vibration data was collected while the wind turbine was in operation. Tower vibration signals were analyzed using Empirical Mode Decomposition and the outcomes were correlated with the vibration signals acquired directly from the generator bearings. It is shown that the generator bearing fault signatures were present in the vibrations from the tower. The results suggest that useful condition monitoring of nacelle components can be done even when there is no condition monitoring system installed on the generator bearings, as is often the case for older wind turbines. In the second part of the paper, acoustic measurements from a healthy and a faulty wind turbine are shown. The preliminary analysis suggests that the generator bearing fault increases the overall sound pressure level at the bottom of the tower, and is not buried in the background noise.

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“…Moreover the result of EEMD decomposition is not necessarily the standard IMF, but the problem of mode splitting may also occur, that is, the same physical process is divided into multiple IMF components. Therefore, given the chaotic sequence of landslide deformation, this paper uses MEEMD to decompose, and the detailed decomposition process is shown in the literature [20][21][22].…”

Section: Modified Ensemble Empirical Mode Decompositionmentioning

confidence: 99%

“…First MEEMD [20][21][22] was used to decompose the non-stationary landslide time series into a series of different characteristic scales of intrinsic mode function (IMF). Then the approximate entropy [23,24] was adopted for the complexity analysis of each component, producing a new subsequence through combination stacking according to the different entropy values.…”

Section: Introductionmentioning

confidence: 99%

Combined Forecasting Method of Landslide Deformation Based on MEEMD, Approximate Entropy, and WLS-SVM

Xie

Liang

Zhong

et al. 2017

IJGI

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Abstract:Given the chaotic characteristics of the time series of landslides, a new method based on modified ensemble empirical mode decomposition (MEEMD), approximate entropy and the weighted least square support vector machine (WLS-SVM) was proposed. The method mainly started from the chaotic sequence of time-frequency analysis and improved the model performance as follows: first a deformation time series was decomposed into a series of subsequences with significantly different complexity using MEEMD. Then the approximate entropy method was used to generate a new subsequence for the combination of subsequences with similar complexity, which could effectively concentrate the component feature information and reduce the computational scale. Finally the WLS-SVM prediction model was established for each new subsequence. At the same time, phase space reconstruction theory and the grid search method were used to select the input dimension and the optimal parameters of the model, and then the superposition of each predicted value was the final forecasting result. Taking the landslide deformation data of Danba as an example, the experiments were carried out and compared with wavelet neural network, support vector machine, least square support vector machine and various combination schemes. The experimental results show that the algorithm has high prediction accuracy. It can ensure a better prediction effect even in landslide deformation periods of rapid fluctuation, and it can also better control the residual value and effectively reduce the error interval.

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Accent extraction of emotional speech based on modified ensemble empirical mode decomposition

Cited by 15 publications

References 12 publications

The Use of Ensemble Empirical Mode Decomposition With Canonical Correlation Analysis as a Novel Artifact Removal Technique

The Use of Ensemble Empirical Mode Decomposition With Canonical Correlation Analysis as a Novel Artifact Removal Technique

Indicative Fault Diagnosis of Wind Turbine Generator Bearings Using Tower Sound and Vibration

Combined Forecasting Method of Landslide Deformation Based on MEEMD, Approximate Entropy, and WLS-SVM

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