# how to extract a radio signal features with python?

Signal Processing Asked by nechi on December 8, 2020

I have a dataset of radio signals that i want to classify. So, I decided to extract features. After some googling, I have noticed that I have to calculate moments, cumulants, Kutosis and skewness to use them as features.

for the moments I found that there is a function called scipy.stats.moment/. But then, I found another code :

def moment(sample, p, q):
m = np.mean((sample**(p-q)) * (np.conjugate(sample)**q))
return m


what is the difference between the 2 functions? and do I calculate the cumulants basing on the moments?

for the skewness and Kurtosis what do they really represents?
Are there any other features that could help?

Some of the more cutting edge signal classification techniques utilize deep neural networks (DNNs):

https://arxiv.org/pdf/1712.04578.pdf

This particular paper utilizes a convolutional neural network (CNN) and a residual neural network. The convolutional "front-end" extracts its own nonlinear features prior to connection to the densely connected layers.

DNNs are typically very computationally expensive, so your approach to feature extraction approach might meet your requirements.

Answered by Nasty on December 8, 2020

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