# Empirical mode decomposition as a filter bank

@article{Flandrin2004EmpiricalMD, title={Empirical mode decomposition as a filter bank}, author={Patrick Flandrin and Gabriel Rilling and Paulo Gonçalves}, journal={IEEE Signal Processing Letters}, year={2004}, volume={11}, pages={112-114} }

Empirical mode decomposition (EMD) has recently been pioneered by Huang et al. for adaptively representing nonstationary signals as sums of zero-mean amplitude modulation frequency modulation components. In order to better understand the way EMD behaves in stochastic situations involving broadband noise, we report here on numerical experiments based on fractional Gaussian noise. In such a case, it turns out that EMD acts essentially as a dyadic filter bank resembling those involved in wavelet…

## 2,244 Citations

### Empirical Mode Decompositions as Data-Driven Wavelet-like Expansions

- PhysicsInt. J. Wavelets Multiresolution Inf. Process.
- 2004

The experimental spectral analysis and statistical characterization of the obtained modes reveal an equivalent filter bank structure which shares most properties of a wavelet decomposition in the same context, in terms of self-similarity, quasi-decorrelation and variance progression.

### Application of Empirical Mode Decomposition in Denoising a Speech Signal

- Computer Science
- 2014

This paper attempts to review and summarize the use of Empirical mode decomposition (EMD) for denoising a speech signal. EMD, introduced by Huang et al in 1998 gives time-frequency representation of…

### Absolute value Empirical Mode Decomposition

- Computer Science2011 4th International Congress on Image and Signal Processing
- 2011

The A-EMD approach can detect a new set of Intrinsic Mode Functions, which clearly present the location of the wave peaks and can quickly detect the jump points for the jump signals.

### Hybrid wavelet-Hilbert-Huang spectrum analysis

- EngineeringEurope Oceans 2005
- 2005

Empirical mode decomposition (EMD) is a new method pioneered by Huang et al. for non-linear and non-stationary signal analysis. Signals are adaptively decomposed into several zero-mean amplitude…

### Iterative Filtering as an Alternative Algorithm for Empirical Mode Decomposition

- Computer ScienceAdv. Data Sci. Adapt. Anal.
- 2009

This paper proposes an alternative algorithm for EMD based on iterating certain filters, such as Toeplitz filters, which yields similar results as the more traditional sifting algorithm, and can be rigorously proved.

### Filter Bank Property of Multivariate Empirical Mode Decomposition

- Computer Science, EngineeringIEEE Transactions on Signal Processing
- 2011

It is found that, similarly to EMD, MEMD also essentially acts as a dyadic filter bank on each channel of the multivariate input signal, but better aligns the corresponding intrinsic mode functions from different channels across the same frequency range which is crucial for real world applications.

### EMD Equivalent Filter Banks, from Interpretation to Applications

- Geology
- 2005

Huang's data-driven technique of empirical mode decomposition is given a filter bank interpretation from two complementary perspectives and a stochastic approach shows the spontaneous emergence of an equivalent dyadic filter bank structure when EMD is applied to the versatile class of fractional Gaussian noise processes.

### Enhanced Empirical Mode Decomposition

- PhysicsICCSA
- 2008

Bandpass enhanced EMD is applied to a bat chirp signal and the fundamental, the first, second, and part of the third harmonic are expressed, demonstrating the improved sensitivity of this method over the standard HHT approach.

### Empirical mode decomposition of voiced speech signal

- EngineeringFirst International Symposium on Control, Communications and Signal Processing, 2004.
- 2004

This paper describes a new technique, called the empirical mode decomposition (EMD), for adaptively representing nonstationary signals as sums of zero-mean AM-FM components that allows the analysis of frequency composition of one-dimensional signals.

### Fast ensemble empirical mode decomposition for speech-like signal analysis using shaped noise addition

- Computer ScienceThe 4th International Conference on Interaction Sciences
- 2011

The experimental results show that both pink noise and brown noise outperform the white noise in terms of computation for the EEMD of speech-like signal, and the signal-spectrum-dependent noise is able to effectively randomize the targeted signal in time domain, and then significantly save the superfluous calculation around the corresponding energy-free frequencies.

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