Targeted noise removal by seismic time-frequency masking (STFM) and minimum statistics approach

2021 ◽  
Author(s):  
Andrey Bakulin ◽  
Dmitry Neklyudov ◽  
Ilya Silvestrov
Keyword(s):  
Noise Removal ◽  
Time Frequency ◽  
Minimum Statistics

ECG Signal Denoising Algorithm Based on Optimization Wavelet Transform

2013 ◽  
Vol 706-708 ◽  
pp. 785-788
Author(s):  
Guo Shun Yuan ◽  
Li Qing Geng
Keyword(s):  
Wavelet Transform ◽  
Median Filter ◽  
Noise Removal ◽  
Signal Denoising ◽  
Time Frequency ◽  
Multi Resolution Analysis ◽  
Signal Characteristics ◽  
Eigen Values ◽  
Resolution Analysis ◽  
Ecg Noise Removal

Wavelet transform algorithm with its unique multi-resolution analysis and it is in the time - frequency domain has the advantage of the ability to characterize the local signal characteristics, let it has been widely used in signal detection, noise removal, feature extraction, image compression and so on. In this paper, on the basis of already wavelet transform ECG noise removal, proposed a median filter optimization algorithm, enables ECG noise removal effect is more obvious, also for the Eigen values detection of ECG lay a better foundation.

scholarly journals Quantitative Determination of the Multicomponents With Overlapping Ultraviolet Spectra Using Wavelet-Packed Transform and Partial Least Squares

2006 ◽  
Vol 2006 ◽  
pp. 1-6
Author(s):  
Ling Gao ◽  
Shouxin Ren
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Least Squares Method ◽  
Wavelet Packet ◽  
Noise Removal ◽  
Time Frequency ◽  
Relative Standard ◽  
Best Basis Algorithm ◽  
Partial Least Squares Method

This paper presented a novel method named wavelet packet transform-based partial least squares method (WPTPLS) for simultaneous spectrophotometric determination ofα-naphthylamine, p-nitroaniline, and benzidine. Wavelet packet representations of signals provided a local time-frequency description and separation ability between information and noise. The quality of the noise removal can be improved by using best-basis algorithm and thresholding operation. Partial least squares (PLS) method uses both the response and concentration information to enhance its ability of prediction. In this case, by optimization, wavelet function and decomposition level for WPTPLS method were selected as Db16 and 3, respectively. The relative standard errors of prediction (RSEP) for all components with WPTPLS and PLS were 2.23% and 2.71%, respectively. Experimental results showed WPTPLS method to be successful and better than PLS.

Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. V211-V227 ◽  
Author(s):  
S. Mostafa Mousavi ◽  
Charles A. Langston
Keyword(s):  
Seismic Data ◽  
Ambient Noise ◽  
Cross Validation ◽  
Single Channel ◽  
Threshold Level ◽  
Noise Removal ◽  
Seismic Signals ◽  
Hard Thresholding ◽  
Resolution Time ◽  
Time Frequency

Recorded seismic signals are often corrupted by noise. We have developed an automatic noise-attenuation method for single-channel seismic data, based upon high-resolution time-frequency analysis. Synchrosqueezing is a time-frequency reassignment method aimed at sharpening a time-frequency picture. Noise can be distinguished from the signal and attenuated more easily in this reassigned domain. The threshold level is estimated using a general cross-validation approach that does not rely on any prior knowledge about the noise level. The efficiency of the thresholding has been improved by adding a preprocessing step based on kurtosis measurement and a postprocessing step based on adaptive hard thresholding. The proposed algorithm can either attenuate the noise (either white or colored) and keep the signal or remove the signal and keep the noise. Hence, it can be used in either normal denoising applications or preprocessing in ambient noise studies. We tested the performance of the proposed method on synthetic, microseismic, and earthquake seismograms.

scholarly journals Application of improved time–frequency representation to local noise removal

2021 ◽  
Vol 11 (5) ◽  
pp. 2091-2096
Author(s):  
Baotong Liu ◽  
Qiyuan Liu ◽  
Xuefu Kang
Keyword(s):  
Temporal Resolution ◽  
Real Data ◽  
Noise Removal ◽  
Coherent Noise ◽  
Exponential Factor ◽  
Time Frequency ◽  
Coupling Noise ◽  
Frequency Representation ◽  
S Transform ◽  
Parameter Values

AbstractThe temporal resolution of conventional S transform (ST) is not sufficient for the separation of local coherent noise. We present a revised S transform (RST) which uses an analyzing window function with two control parameters of the scalar σ and the exponential factor γ. Selecting proper parameter values (say σ = 1.1, γ = 1.08), the time–frequency representation (TFR) acquired by our method exhibits a higher temporal resolution. Applying an appropriate filter in the time–frequency domain, we are able to remove specific local noise. Distributed acoustic sensing (DAS) VSP section may suffer from fiber cable coupling noise, hindering the subsequent data processing and geologic interpretation. The real data example shows the coupling noise occurred in the DAS VSP can be removed by the presented RST.

Seismic Data Denoising Simulation Research Based on Wavelet Transform

2014 ◽  
Vol 490-491 ◽  
pp. 1356-1360 ◽  
Author(s):  
Shu Cong Liu ◽  
Er Gen Gao ◽  
Chen Xun
Keyword(s):  
Wavelet Transform ◽  
Seismic Data ◽  
Wavelet Packet ◽  
Real Data ◽  
Noise Removal ◽  
Wavelet Packet Transform ◽  
Seismic Signals ◽  
Transform Method ◽  
Simulation Research ◽  
Time Frequency

The wavelet packet transform is a new time-frequency analysis method, and is superior to the traditional wavelet transform and Fourier transform, which can finely do time-frequency dividion on seismic data. A series of simulation experiments on analog seismic signals wavelet packet decomposition and reconstruction at different scales were done by combining different noisy seismic signals, in order to achieve noise removal at optimal wavelet decomposition scale. Simulation results and real data experiments showed that the wavelet packet transform method can effectively remove the noise in seismic signals and retain the valid signals, wavelet packet transform denoising is very effective.

scholarly journals A minireview on the use of wavelet analyses on physiological signals to diagnose and characterize ADHD

2017 ◽  
Vol 6 (3) ◽  
pp. 57 ◽  
Author(s):  
Benito De Celis Alonso ◽  
Javier M. Hernández López ◽  
José G. Suárez García ◽  
Eduardo Moreno Barbosa
Keyword(s):  
Imaging System ◽  
Noise Removal ◽  
Mathematical Tool ◽  
Learning Networks ◽  
Adhd Diagnosis ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Golden Standard ◽  
Two Parameters ◽  
Wavelet Analyses

Attention deficit hyperactivity disorder (ADHD) is one of the most prevalent psychological disorders in pediatric patients. The actual golden standard of ADHD diagnosis is based on conclusions derived from clinical questionnaires. Nowadays, there is no quantitative measurement performed with any imaging system (MRI, PET, EEG, etc.) that can be considered as a golden standard for this diagnosis. This issue, is highlighted by the existence of international competitions focused on the production of a technological (quantitative) solution capable of complementing ADHD diagnosis (ADHD-200 Global Competition). Wavelet analysis, on the other hand, is a flexible mathematical tool that can be used for information and data processing. Its advantage over other types of mathematical transformations is its ability to decompose a signal into two parameters (frequency and time). Based on the prevalence of ADHD and the extra functionality of wavelet tools, this review will try to answer the following question: How have wavelet analyses been used to complement diagnosis and characterization of ADHD? It will be shown that applications were not casual and limited to time-frequency decomposition, noise removal or down sampling of signals, but were pivotal for construction of learning networks, specific parameterization of signals or calculations of connectivity between brain nodes.

Harmonic noise removal in united time-frequency domain via sparse promotion

2015 ◽  
Author(s):  
Xiangfang Li ◽  
Wenchao Chen* ◽  
Wei Wang ◽  
Xiaokai Wang ◽  
Yanhui Zhou
Keyword(s):  
Frequency Domain ◽  
Noise Removal ◽  
Time Frequency ◽  
Harmonic Noise

scholarly journals A Fast Adaptive Speech Extraction Method using Blind Source Separation for Audio Signal Processing

2020 ◽  
Vol 9 (4) ◽  
pp. 727-733
Keyword(s):  
Signal Processing ◽  
Extraction Method ◽  
Statistical Approach ◽  
Audio Signal ◽  
Noise Removal ◽  
Adaptive Signal Processing ◽  
Audio Signal Processing ◽  
Time Frequency ◽  
Signal Processing Methods ◽  
Adaptive Signal

The adaptive signal processing methods are used in several applications like channel estimation, Noise removal and extraction of signals also. The methods vary on time, frequency and statistical approach. In this paper, the source speech signals are separated using different methods like FastICA,PCA and kICA. Comparison of original signal and estimated signals are evaluated for different methods. The implementation was done in MATLAB. The spectrogram, Negentropy and Kurtosis waveforms are plotted for different methods.

scholarly journals Seismic random noise removal by delay-compensation time-frequency peak filtering

2017 ◽  
Vol 14 (3) ◽  
pp. 691-697 ◽  
Author(s):  
Pengjun Yu ◽  
Yue Li ◽  
Hongbo Lin ◽  
Ning Wu
Keyword(s):  
Random Noise ◽  
Noise Removal ◽  
Delay Compensation ◽  
Time Frequency ◽  
Frequency Peak
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