Computation of Hilbert-Huang Spectrum

MATLAB 819B 303 views 0 downloads 1 credits
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Resource Overview

Implementation program for Hilbert-Huang Spectrum calculation, where extracted spectral information can be applied to speech recognition, fault detection, and other signal processing applications. The algorithm involves Empirical Mode Decomposition (EMD) and Hilbert spectral analysis for time-frequency feature extraction.

Detailed Documentation

This article provides a detailed explanation of the computational procedures for Hilbert-Huang Spectrum analysis. The program performs versatile functions in extracting spectral characteristics from signals, with applications spanning speech recognition, fault detection, and audio processing. The implementation employs Empirical Mode Decomposition (EMD) to decompose nonlinear signals into intrinsic mode functions (IMFs), followed by Hilbert transform applied to each IMF component to obtain instantaneous frequency data. Through this signal transformation approach, the algorithm effectively captures time-frequency features that reveal fundamental signal properties. We will elaborate on the computational implementation methodology, including key functions for EMD decomposition and Hilbert spectral analysis, along with practical applications across various domains.

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