Confronta i metodi

	Pulizia del segnale mediante wavelet (soglia morbida)	Decomposizione Empirica dei Modi (EMD)	Decomposizione Modale Variazionale (VMD)
Campo	Elaborazione dei segnali	Elaborazione dei segnali	Elaborazione dei segnali
Famiglia	Machine learning	Machine learning	Machine learning
Anno di origine	1995	1998	2014
Ideatore	David Donoho	Norden Huang et al.	Konstantin Dragomiretskiy & Dominique Zosso
Tipo	Non-parametric signal estimation	Adaptive data-driven decomposition algorithm	Adaptive variational signal decomposition algorithm
Fonte seminale	Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41(3), 613–627.	Huang, N. E., et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A, 454(1971), 903–995.	Dragomiretskiy, K., & Zosso, D. (2014). Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3), 531–544.
Alias	Wavelet Shrinkage, Donoho-Johnstone Denoising, Soft Thresholding Denoising, Sinyal Gürültü Giderme	EMD, Intrinsic Mode Decomposition, Adaptive Signal Decomposition, Ampirik Mod Ayrıştırma	VMD, Adaptive Signal Decomposition, Variational Signal Decomposition, Varyasyonel Mod Ayrıştırma
Correlati	3	3	2
Sintesi	Wavelet signal denoising, introduced by David Donoho in 1995, is a non-parametric technique that removes noise from one-dimensional or multidimensional signals by decomposing them into wavelet coefficients, suppressing small coefficients that likely represent noise via a soft-thresholding operator, and reconstructing a smooth estimate. It is widely used in biomedical signal processing, geophysics, audio engineering, and image analysis where the underlying signal is assumed to be sparse or piecewise smooth.	Empirical Mode Decomposition (EMD) is a fully data-driven, adaptive method for decomposing nonlinear and non-stationary time series into a finite set of oscillatory components called Intrinsic Mode Functions (IMFs), plus a monotonic residue. Introduced by Norden E. Huang and colleagues at NASA in 1998, EMD requires no predefined basis functions and derives all components directly from the signal itself, making it fundamentally different from Fourier or wavelet transforms.	Variational Mode Decomposition (VMD) is a fully adaptive, non-recursive signal decomposition method introduced by Konstantin Dragomiretskiy and Dominique Zosso in 2014. It decomposes a real-valued input signal into a discrete number of sub-signals, called intrinsic mode functions (IMFs), each with a specific sparsity in the frequency domain. Unlike Empirical Mode Decomposition, VMD frames decomposition as a variational optimization problem solved via the Alternating Direction Method of Multipliers (ADMM), yielding robust and physically meaningful components.