The Hilbert-Huang Transform | combining Empirical Mode Decomposition and Hilbert Spectrum

This video explains the Hilbert-Huang Transform of discrete real-valued data. For this approach, the data is pre-processed by an empirical mode decomposition and the Hilbert Transform is applied to each of the resulting modes individually. All resulting Hilbert spectra are added to obtain a single Hilbert spectrum for the complete data. Based on examples, it is shown that this approach is superior in precisely detecting the frequency and energy content of the data compared to the standard procedure and the reasons for this are also discussed. For a better understanding of this content, I highly recommend to first get familiar with the empirical mode decomposition and the Hilbert Transform. EMD:    • Empirical Mode Decomposition (1D, univaria...   HT:    • Hilbert Transform & Hilbert Spectrum | und...   -------------------------------------------------------------------------------------------------------------------------------------- References: Huang et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 454(1971), 903-995. Lyons, R. (2001). Understanding digital signal processing's frequency domain. RF DESIGN, 24(11), 36-49. [ https://www.researchgate.net/publicat... ] -------------------------------------------------------------------------------------------------------------------------------------- Time stamps: 0:00 Recap of Hilbert Transform & Hilbert Spectrum 3:11 Introduction to the Hilbert-Huang Transform 3:50 Synthetic example 5:33 Why is it superior? 9:31 Back to synthetic example 10:33 Real-world example