EMD = Empirical Mode Decomposition

Description

EMD is a method for analysing non-stationary and nonlinear data. I'm going to tell you main things about EMD:

• Method is locally adaptive, data-driven, multiscale, high efficient.
• The user specifies the number of mod.
• Fast oscillations superimposed to slow oscillations (First mode = fast oscillations = high frequency. Last mode = slow oscillations = low frequency).
• Many applications to speech analysis (biological data, astronomical data, nonlinear physics data, earthquake, climate, etc.).

Motivation

Test Data

We are going to use noise sinus:

	noise = random.uniform(-0.05,0.05,10000)
	signal = sin(2*pi*f*t) + noise

Results

If number of mod = 2

If number of mod = 4

Result EMD for Van der Pol oscillator. The number of mod = 4.

Learn more

• Wiki EMD
• scipy.interpolate.splrep
• scipy.interpolate.splev

< I'm going to add some useful links lately...

Installation

You can use Python with data package: Anaconda or Miniconda. There's another way - use Portable Python. Also you can use whatever IDE for Python.

License

Free