Abstract—This paper presents a denoising method called EMDCustom that was based on Empirical Mode Decomposition (EMD) and the modified Customized Thresholding Function (Custom) algorithms. EMD was applied to decompose adaptively a noisy signal into intrinsic mode functions (IMFs). Then, all the noisy IMFs got threshold by applying the presented thresholding function to suppress noise and to imp...

This paper proposes an EMD-LSSVM (empirical mode decomposition least squares support vector machine) model to analyze the CSI 300 index. A WD-LSSVM (wavelet denoising least squares support machine) is also proposed as a benchmark to compare with the performance of EMD-LSSVM. Since parameters selection is vital to the performance of the model, different optimization methods are used, including s...

Watermarking an audio signal is a very crucial thing. So a new watermarking technique Empirical Mode Decomposition (EMD) is introduced. The audio signal is first split into frames and each one is then decomposed by EMD to get intrinsic periodic components known as Intrinsic Mode Functions (IMFs). The combination of watermark and synchronization code are embedded into the last IMF, which is most...

Within a combined EEG-fMRI study of contour integration, we analyze responses to Gabor stimuli with a combined Empirical Mode Decomposition and an Independent Component Analysis. Generaly, responses to different stimuli are very similar thus hard to differentiate. EMD and ICA are used intermingled and not simply in a sequential way. This novel combination helps to suppress redundant modes resul...

Within a combined EEG-fMRI study of contour integration, we analyze responses to Gabor stimuli with an Empirical Mode Decomposition combined with an Independent Component Analysis. Generally, responses to different stimuli are very similar thus hard to differentiate. EMD and ICA are used intermingled and not simply in a sequential way. This novel combination helps to suppress redundant modes re...

This paper presents an application of Empirical Mode Decomposition (EMD) on the induction of notated tempo from music recordings. At a first stage, EMD is employed as a means to segment music recordings into segments that exhibit similar rhythmic characteristics. At a second stage, EMD is used in order to analyze the diagonals of the Self-Similarity Matrix of each segment, so as to estimate the...

Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based ...

We report here on image texture analysis and on numerical simulation of fractional Brownian textures based on the newly emerged Empirical Mode Decomposition (EMD). EMD introduced by N.E. Huang et al. is a promising tool to non-stationary signal representation as a sum of zero-mean AM-FM components called Intrinsic Mode Functions (IMF). Recent works published by P. Flandrin et al. relate that, i...

Sparse representation has long been studied and several dictionary learning methods have been proposed. The dictionary learning methods are widely used because they are adaptive. In this paper, a new dictionary learning method for audio is proposed. Signals are at first decomposed into different degrees of Intrinsic Mode Functions (IMF) using Empirical Mode Decomposition (EMD) technique. Then t...

This paper discusses some mat hematical issues related to empirical mode decomposition (EMD). A B-spline EMD algorithm is introduced and developed for the convenience of mathematical studies. The numerical analysis using both simulated and practical signals and application examples from vibration analysis indicate that the B-spline algorithm has a comparable performance to that of the original ...