scholarly journals Comparative Study between the Discrete-Frequency Kalman Filtering and the Discrete-Time Kalman Filtering with Application in Noise Reduction in Speech Signals

2018 ◽  
Vol 2018 ◽  
pp. 1-5
Author(s):  
Leandro Aureliano da Silva ◽  
Gilberto Arantes Carrijo ◽  
Eduardo Silva Vasconcelos ◽  
Roberto Duarte Campos ◽  
Cleiton Silvano Goulart ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Comparative Study ◽  
Noise Reduction ◽  
Discrete Time ◽  
Kalman Filtering ◽  
Signal To Noise Ratio ◽  
Spectral Distortion ◽  
Signal To Noise ◽  
Discrete Frequency ◽  
Speech Reconstruction

This article aims to carry out a comparative study between discrete-time and discrete-frequency Kalman filters. In order to assess the performance of both methods for speech reconstruction, we measured the output segmental signal-to-noise ratio and the Itakura-Saito distance provided by each algorithm over 25 different voice signals. The results show that although the two algorithms performed very similarly regarding noise reduction, the discrete-time Kalman filter produced smaller spectral distortion on the estimated signals when compared with the discrete-frequency Kalman filter.

scholarly journals Noise reduction and signal-to-noise ratio improvement of atomic magnetometers with optical gradiometer configurations

2015 ◽  
Vol 23 (5) ◽  
pp. 6976 ◽  
Author(s):  
Keigo Kamada ◽  
Yosuke Ito ◽  
Sunao Ichihara ◽  
Natsuhiko Mizutani ◽  
Tetsuo Kobayashi
Keyword(s):  
Noise Reduction ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Noise Ratio

Threshold Noise Reduction Research of Improved EEMD Method

2012 ◽  
Vol 226-228 ◽  
pp. 237-240 ◽  
Author(s):  
Mei Jun Zhang ◽  
Hao Chen ◽  
Chuang Wang ◽  
Qing Cao
Keyword(s):  
Noise Reduction ◽  
Signal To Noise Ratio ◽  
Frequency Noise ◽  
Signal To Noise ◽  
Time Frequency ◽  
Noise Ratio ◽  
Reduction Effect ◽  
Eemd Method ◽  
Simulation Signal ◽  
Improved Eemd

In order to extract effectively detection signals in the noise background for non-stationary signal.On the basis of EEMD, improved EEMD is put forward, the improve EEMD threshold noise reduction is researched in this paper.The simulation signal compared the noise reduction effect of the wavelet,EMD,EEMD,and the improved EEMD. The improved EEMD threshold noise reduction have the best noise reduction result , the highest signal-to-noise ratio, the smallest standard deviation error.After the improved EEMD threshold noise reduction , the measurement signal time domain waveform smooth. More high frequency noise was obviously reduced in Hilbert time- frequency spectrum. Signal-to-noise ratio significantly improve, and signal characteristics are very clear.

SYNCHRONIZATION THROUGH FILTERING

2000 ◽  
Vol 10 (04) ◽  
pp. 763-775 ◽  
Author(s):  
C. CRUZ ◽  
H. NIJMEIJER
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Discrete Time ◽  
Kalman Filtering ◽  
Private Communication ◽  
Extended Kalman Filtering ◽  
Extensive Simulation ◽  
Synchronization Problem

We study the synchronization problem in discrete-time via an extended Kalman filter (EKF). That is, synchronization is obtained of transmitter and receiver dynamics in case the receiver is given via an EKF that is driven by a noisy drive signal from a noisy transmitter dynamics. The convergence of the filter dynamics towards the transmitter dynamics is rigorously shown using recent results in extended Kalman filtering. Two extensive simulation examples show that the filter is indeed suitable for synchronization of (noisy) chaotic transmitter dynamics. An application to private communication is also given.

scholarly journals Multistage noise reduction processing for vibration signal of hydropower units

2021 ◽  
Vol 2108 (1) ◽  
pp. 012008
Author(s):  
Yousong Shi ◽  
Jianzhong Zhou
Keyword(s):  
Noise Reduction ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Field Testing ◽  
Signal To Noise ◽  
Vibration Signals ◽  
Multi Stage ◽  
Actual Field ◽  
Stationary Vibration ◽  
Testing Environments

Abstract In actual field testing environments of hydropower units, unit vibration signals are often contaminated with noise. In order to obtain the real vibration signal, a multi-stage vibration signal denoise method SG-SVD-VMD is proposed for the guide bearing nonlinear and non-stationary vibration signals. And the root mean square error (RMSE) and signal to noise ratio (SNR) are used to evaluate the noise reduction ability of eight methods. The results show that the noise-canceling ability of this proposed method has improved to some extent. It can effectively suppress the noise of the hydropower units vibration signals. This method can effectively identify the shaft track and the running state of hydropower units.

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