A Classic Example of Empirical Mode Decomposition
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Empirical Mode Decomposition (EMD) is a classical signal processing method that decomposes signals into multiple Intrinsic Mode Functions (IMFs), revealing local characteristics and frequency information within the signal. The complete EMD processing workflow can be automatically demonstrated, covering the entire process from signal decomposition to final result visualization. The algorithm typically involves iterative sifting processes to extract IMFs through local extrema detection, envelope construction using spline interpolation, and mean envelope subtraction. Key implementation steps include identifying local maxima/minima, fitting upper/lower envelopes, and verifying IMF conditions through stoppage criteria. Code implementations often utilize numerical computation libraries for efficient extremum detection and interpolation operations.
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