# 3. Quick overview of spectral analysis methods¶

This section gives you a quick overview of the spectral analysis methods and classes that are available in spectrum. You will find the different classes associated to each PSD estimates. A functional approach is also possible but is not described here. See the reference guide for more details.

## 3.1. Non-parametric classes¶

The Fourier-based methods provides `Periodogram`, `pcorrelogram`, Welch estimate (not implemented see pylab.psd instead) and multitapering `pmtm`.

In addition to the Fourier-based methods, there are 3 types of non-parametric methods:

1. The Minimum of variance MV (Capon) is implemented in the class `pminvar`.
2. Two eigenvalues decomposition (MUSIC, eigenvalue) can be found in `pev` and `pmusic`.
3. Maximum entropy (MEM) (not yet implemented)

## 3.2. Autoregressive spectral estimation¶

There are essentially 3 methods to estimate the autoregressive (AR) parameters. The first one uses the autocorrelation sequence such as in the so-called Yule-Walker method (see `pyule`). A second method uses the reflection coefficient method such as in the Burg algorithm (see `pburg`). These methods minimise the forward prediction error (and backward) using Levinson recursion. Finally, a third important category of AR parameter method is based on the least squares linear prediction, which can be further decomposed into 2 categories. One that separate the minimization of the forward and backward linear prediction squared errors such as the autocorrelation or covariance methods (see `pcovar`). Another one that performs a combined minimization of the forward and backward prediction squared errors (modified covariance) (see `pmodcovar`).

Spectrum also provides `parma`, `pma` classes.