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Dominant Component Tracking for Empirical Mode Decomposition using A Hidden Markov Model | IEEE Conference Publication | IEEE Xplore

Dominant Component Tracking for Empirical Mode Decomposition using A Hidden Markov Model


Abstract:

It is well known that the empirical mode decomposition algorithm does not always return an appropriate decomposition due to problems like mode mixing. In this paper, we c...Show More

Abstract:

It is well known that the empirical mode decomposition algorithm does not always return an appropriate decomposition due to problems like mode mixing. In this paper, we consider the problem of a component being split across several intrinsic mode functions (IMFs). We propose the use of a hidden Markov model (HMM) to track the dominant component across the set of IMFs returned by EMD. We provide an example demonstrating the proposed tracking using an acoustic recording where component splitting is present in the decomposition and compare our method to two other possible tracking approaches. We show that the proposed method provides a compromise between smoothness and energy associated with the tracked component.
Date of Conference: 26-29 November 2018
Date Added to IEEE Xplore: 21 February 2019
ISBN Information:
Conference Location: Anaheim, CA, USA

References

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