Performance analysis of adaptive variational mode decomposition approach for speech enhancement

2018 ◽  
Vol 21 (2) ◽  
pp. 369-381 ◽  
Author(s):  
Rashmirekha Ram ◽  
Mihir Narayan Mohanty
Keyword(s):  
Performance Analysis ◽  
Speech Enhancement ◽  
Variational Mode Decomposition ◽  
Decomposition Approach ◽  
Mode Decomposition

Applications of variational mode decomposition in seismic time-frequency analysis

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. V365-V378 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Yangkang Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Synthetic Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Decomposition Approach ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.

Speech Enhancement: A Multivariate Empirical Mode Decomposition Approach

2013 ◽  
pp. 192-199 ◽  
Author(s):  
Jordi Solé-Casals ◽  
Esteve Gallego-Jutglà ◽  
Pere Martí-Puig ◽  
Carlos M. Travieso ◽  
Jesús B. Alonso
Keyword(s):  
Empirical Mode Decomposition ◽  
Speech Enhancement ◽  
Decomposition Approach ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition

A variant of SWEMDH technique based on variational mode decomposition for speech enhancement

2021 ◽  
Vol 25 (3) ◽  
pp. 299-308
Author(s):  
Poovarasan Selvaraj ◽  
E. Chandra
Keyword(s):  
White Noise ◽  
Speech Enhancement ◽  
Speech Signal ◽  
High Frequency ◽  
Hurst Exponent ◽  
Sliding Window ◽  
Variational Mode Decomposition ◽  
Vocal Signal ◽  
Mode Decomposition ◽  
Vocal Signals

In Speech Enhancement (SE) techniques, the major challenging task is to suppress non-stationary noises including white noise in real-time application scenarios. Many techniques have been developed for enhancing the vocal signals; however, those were not effective for suppressing non-stationary noises very well. Also, those have high time and resource consumption. As a result, Sliding Window Empirical Mode Decomposition and Hurst (SWEMDH)-based SE method where the speech signal was decomposed into Intrinsic Mode Functions (IMFs) based on the sliding window and the noise factor in each IMF was chosen based on the Hurst exponent data. Also, the least corrupted IMFs were utilized to restore the vocal signal. However, this technique was not suitable for white noise scenarios. Therefore in this paper, a Variant of Variational Mode Decomposition (VVMD) with SWEMDH technique is proposed to reduce the complexity in real-time applications. The key objective of this proposed SWEMD-VVMDH technique is to decide the IMFs based on Hurst exponent and then apply the VVMD technique to suppress both low- and high-frequency noisy factors from the vocal signals. Originally, the noisy vocal signal is decomposed into many IMFs using SWEMDH technique. Then, Hurst exponent is computed to decide the IMFs with low-frequency noisy factors and Narrow-Band Components (NBC) is computed to decide the IMFs with high-frequency noisy factors. Moreover, VVMD is applied on the addition of all chosen IMF to remove both low- and high-frequency noisy factors. Thus, the speech signal quality is improved under non-stationary noises including additive white Gaussian noise. Finally, the experimental outcomes demonstrate the significant speech signal improvement under both non-stationary and white noise surroundings.

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