Generalized filter-bank equalizer for noise reduction with reduced signal delay

2005 ◽  
Author(s):  
Heinrich W. Lollmann ◽  
Peter Vary
Keyword(s):  
Noise Reduction ◽  
Filter Bank ◽  
Signal Delay

Two-Stage Filter-Bank System for Improved Single-Channel Noise Reduction in Hearing Aids

2015 ◽  
Vol 23 (2) ◽  
pp. 383-393 ◽  
Author(s):  
Alexander Schasse ◽  
Timo Gerkmann ◽  
Rainer Martin ◽  
Wolfgang Sorgel ◽  
Thomas Pilgrim ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Filter Bank ◽  
Single Channel ◽  
Channel Noise ◽  
Two Stage ◽  
Bank System

Optimum noise reduction of conjugate quadrature filter bank

2000 ◽  
Vol 80 (5) ◽  
pp. 819-829 ◽  
Author(s):  
Keisuke Sakitani ◽  
Hajime Maeda
Keyword(s):  
Noise Reduction ◽  
Filter Bank ◽  
Quadrature Filter

scholarly journals FOG De-Noising Algorithm Based on Augmented Nonlinear Differentiator and Singular Spectrum Analysis

2018 ◽  
Vol 8 (10) ◽  
pp. 1710 ◽  
Author(s):  
Xiaoming Zhang ◽  
Huiliang Cao ◽  
Xingling Shao ◽  
Jun Liu ◽  
Chong Shen
Keyword(s):  
Noise Reduction ◽  
Correlation Coefficient ◽  
Spectrum Analysis ◽  
Random Noise ◽  
Singular Spectrum Analysis ◽  
Acceleration Factor ◽  
Scale Transformation ◽  
Signal Delay ◽  
Multi Scale ◽  
Singular Spectrum

A novel algorithm based on singular spectrum analysis (SSA) and augmented nonlinear differentiator (AND) for extracting the useful signal from a noisy measurement of fiber optic gyroscope (FOG) is proposed in this paper. As a novel type of tracking differentiator, augmented nonlinear differentiator (AND) has the advantages of dynamical performance and noise-attenuation ability. However, there is a contradiction in AND, i.e., selecting a larger acceleration factor may cause faster convergence but bad random noise reduction, whereas selecting a smaller acceleration factor may lead to signal delay but effective random noise reduction. To overcome the contradiction of AND, multi-scale transformation is introduced. Firstly, the noisy signal is decomposed into components by SSA, and the correlation coefficients between each component and original signal are calculated, then the component with biggest correlation coefficient is reserved and other components are filtered by AND with designed selection criterion of acceleration factor, finally the de-noising result is obtained after reconstruction process. There are mainly two prominent advantages of the proposed SSA-AND algorithm: (i) Compared to traditional tracking differentiators, better de-noising ability can be achieved without signal delay; and (ii) compared to other widely used hybrid de-noising methods based on multi-scale transformation, a parameter determination method is given based on the correlation coefficient of each decomposed component, which improves the reliability of the proposed algorithm.

scholarly journals Delay-Free Tracking Differentiator Design Based on Variational Mode Decomposition: Application on MEMS Gyroscope Denoising

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Xi Zhang ◽  
Huiliang Cao ◽  
Chenguang Wang ◽  
Zhiwei Kou ◽  
Xingling Shao ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Correlation Coefficient ◽  
Random Noise ◽  
Acceleration Factor ◽  
Sigmoid Function ◽  
Variational Mode Decomposition ◽  
Signal Delay ◽  
Tracking Differentiator ◽  
Mode Decomposition ◽  
Useful Signal

This paper presents a delay-free tracking differentiator based on variational mode decomposition (VMD) for extracting the useful signal from a noisy measurement of gyroscope. Sigmoid function-based tracking differentiator (STD) is a novel tracking differentiator with the advantages of noise-attenuation ability and dynamical performance. However, there is a contradiction in STD; i.e., selecting a larger acceleration factor may cause faster convergence but bad random noise reduction whereas selecting a smaller acceleration factor may lead to signal delay but effective random noise reduction. Here, multiscale transformation is introduced to overcome the contradiction of STD. VMD is selected to decompose the noisy signal into multiscale components, and the correlation coefficients between each component and original signal are calculated, then the component with biggest correlation coefficient is reserved and other components are filtered by the proposed adaptive STD algorithm based on the correlation coefficient of each component, and finally the denoising result is obtained after reconstruction. The prominent advantages of the proposed algorithm are as follows: (i) compared to traditional tracking differentiators, better noise suppression ability can be achieved with suppression of time delay; (ii) compared to other widely used denoising methods, a simpler structure but better denoising ability can be obtained.

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