Features of the fourier transform for machine learning

i intend to extract features from time-domain measurement data. I feed the features to machine learning algorithms to detect anomalies.

In the time-domain, i extract mean, RMS, skew and standard deviation. I also want to execute a fourier transform and extract the features from the fourier transform. Intuitively, i would pick the mean frequency and the peak frequency for different frequency bands.

Unfortunately, i cant find any literature on the topic or other people who extracted features from fourier transform (and wavelet, cepstrum, Hilbert, ...) who are smarter than me. can anybody help?

Topic feature-extraction machine-learning

Category Data Science

1
answered at

You can use the actual spectrum as your features. E.g. only select the lowest 10,20,30 frequencies. This approach has been used for e.g. this paper

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