Maximum entropy spectral estimation is a method of spectral density estimation. The goal is to improve the spectral quality based on the principle of maximum entropy. The method is based on choosing the spectrum which corresponds to the most random or the most unpredictable time series whose autocorrelation function agrees with the known values. This assumption, which corresponds to the concept of maximum entropy as used in both statistical mechanics and information theory, is maximally non-committal with regard to the unknown values of the autocorrelation function of the time series. It is simply the application of maximum entropy modeling to any type of spectrum and is used in all fields where data is presented in spectral form. The usefulness of the technique varies based on the source of the spectral data since it is dependent on the amount of assumed knowledge about the spectrum that can be applied to the model.¹⁾

sudo apt-get install python-pip python-matplotlib python-tk python-numpy python-scipy python-sphinx
sudo pip install spectrum  

wget -cS 'https://pypi.python.org/packages/source/s/spectrum/spectrum-.5.6.tar.gz#md5=dee181b34c41f2990b4281ba4998a639'
tar xvf spectrum-0.5.6.tar.gz
cd spectrum-0.5.6
sage -python setup.py install

from spectrum import pburg
import matplotlib.pyplot as plt
import random
 
# Создаём тестовый сигнал
sine_series=[round(sin(2*pi*i/10)+\
                    sin(2*pi*i/2.05)+\
                    sin(2*pi*i/3.333)+\
                    10*random.random()-0.5,4) for i in range(10000)]
 
# Создаём объект спектра
p = pburg(sine_series, 10, NFFT=8192)
 
# Запускаем расчёт спектра
p()
 
# Строим полученный спектр в виде графика
p.plot()
plt.show()

http://homepages.inf.ed.ac.uk/lzhang10/maxent.html#soft

http://thomas-cokelaer.info/software/spectrum/html/contents.html

http://www.oecd-nea.org/tools/abstract/detail/nesc0825