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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?

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  • $\begingroup$ I would assume scipy handles real-valued data, which is a special case of complex data - and your function is supposed to provide moments for a complex variable/signal. $\endgroup$ – M529 Aug 20 at 14:21
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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.

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  • $\begingroup$ Actually I just read this paper. and it is the reason for this question. also I am testing PCA. But, I need theoretical background about how to calculate these features and what is there meaning. $\endgroup$ – nechi Aug 21 at 8:40

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