Comparative Analysis of EMD and VMD Algorithm in Speech Enhancement
Rashmirekha Ram and
Mihir Narayan Mohanty
Additional contact information
Rashmirekha Ram: Department of Electronics and Communication Engineering, Siksha ‘O' Anusandhan University, Bhubaneswar, India
Mihir Narayan Mohanty: Department of Electronics and Communication Engineering, Siksha ‘O' Anusandhan University, Bhubaneswar, India
International Journal of Natural Computing Research (IJNCR), 2017, vol. 6, issue 1, 17-35
Abstract:
Signal enhancement is useful in many areas like social, medicine and engineering. It can be utilized in data mining approach for social and security aspects. Signal decomposition method is an alternative choice due to the elimination of noise and signal enhancement. In this paper, two different algorithms such as Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) are used. The bands are updated concurrently and adaptively in each mode. That performs better than the traditional methods for non-recursive signals. Further it has been investigated that VMD outperforms EMD due to its self-optimization methods as well as adaptively using Wiener filter. It is shown in the result section. Different noise levels as 0dB, 5dB, 10dB and 15dB are considered for input signal.
Date: 2017
References: Add references at CitEc
Citations:
Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJNCR.2017010102 (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:igg:jncr00:v:6:y:2017:i:1:p:17-35
Access Statistics for this article
International Journal of Natural Computing Research (IJNCR) is currently edited by Xuewen Xia
More articles in International Journal of Natural Computing Research (IJNCR) from IGI Global
Bibliographic data for series maintained by Journal Editor ().