Authors - Volodymyr Chumakov, Oksana Kharchenko, Zlatinka Kovacheva, Andrii Poberezhnyi Abstract - The Hilbert–Huang transform is considered. This method is compared to other known methods for handling nonstationary processes, specifically, the windowed Fourier transform and wavelet transform. The comparison is based on real data: the sound radiation of an Unmanned Aerial Vehicle using the example of a small Unmanned Aerial Vehicle, Phantom 4, and real electroencephalograms of a healthy and ill person. The advantages of using the Hilbert–Huang transform over the Hilbert transform are shown, because the latter is used for narrow-band processes. The possibilities of frequency extraction in the case of beats are noted. It is emphasized that Hilbert–Huang transform offers a more adaptive and data-driven approach, allowing it to reveal intrinsic components that traditional methods often obscure. In addition, this method provides a clearer physical interpretation of instantaneous frequencies, which is crucial for studying rapidly changing real-world signals.
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