Abstract
Empirical mode decomposition (EMD) associated with the Hilbert-Huang transform (HHT) deconstructs a time-series signal into a set of monocomponent signals called intrinsic mode functions (IMF). EMD also acts as a filter limiting the frequency range of each IMF. EMD filtering is less than ideal and can lead to misleading results. This difficulty is ameliorated by first subjecting the time-series to bandpass filtration, where the pass-band frequency range is sufficiently narrow that the entire pass-band is captured in a single IMF. A series of such filtrations are required to treat a multicomponent signal. This approach avoids partial representation of those frequencies at the crossover between two successive IMF’s. Bandpass enhanced EMD is applied to a bat chirp signal. That the fundamental, the first, second, and part of the third harmonic are expressed, demonstrates the improved sensitivity of this method over the standard HHT approach. The fundamental and harmonics of this chirp have an exponential form with a decay rate proportional to the square root of the time.
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Donnelly, D. (2008). Enhanced Empirical Mode Decomposition. In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2008. ICCSA 2008. Lecture Notes in Computer Science, vol 5073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69848-7_56
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DOI: https://doi.org/10.1007/978-3-540-69848-7_56
Publisher Name: Springer, Berlin, Heidelberg
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