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Processing stationary noise: model and parameter selection in variational methods [article]

Jérôme Fehrenbach, Pierre Weiss
2013 arXiv   pre-print
We provide a quantitative measure of the distance between a stationary process and the corresponding Gaussian process.  ...  In the second part, we focus on the Gaussian setting and analyze denoising methods which consist of minimizing the sum of a total variation term and an l^2 data fidelity term.  ...  Processing stationary noise: model and parameter selection in  ... 
arXiv:1307.4592v1 fatcat:f3ya5yzdsnf7hcbpw3mhhgwggq

Processing Stationary Noise: Model and Parameter Selection in Variational Methods

Jérôme Fehrenbach, Pierre Weiss
2014 SIAM Journal of Imaging Sciences  
We provide a quantitative measure of the distance between a stationary process and the corresponding Gaussian process.  ...  In the second part, we focus on the Gaussian setting and analyze denoising methods which consist of minimizing the sum of a total variation term and an l 2 data fidelity term.  ...  In a recent paper [8] , a variational method that decomposes an image into the sum of a piecewise smooth component and a set of stationary processes was proposed.  ... 
doi:10.1137/130929424 fatcat:nvts73nof5fhje3avwxp5jtb7u

Model-Based Noise PSD Estimation from Speech in Non-Stationary Noise

Jesper Kjcer Nielsen, Mathew Shaji Kavalekalam, Mads Grcesboll Christensen, Jesper Boldt
2018 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
The results show that the proposed method outperforms state-of-the-art noise PSD estimators in terms of tracking speed and estimation accuracy.  ...  Most speech enhancement algorithms need an estimate of the noise power spectral density (PSD) to work. In this paper, we introduce a model-based framework for doing noise PSD estimation.  ...  Specifically, if we select K = 1 and set the AR-orders to N − 1, then the speech and noise spectral coefficients are modelled as independent and normally distributed random variables and the noise PSD  ... 
doi:10.1109/icassp.2018.8461683 dblp:conf/icassp/NielsenKCB18 fatcat:r2pgt6ixfff27olbzhu5nzsery

Nonstationary filtered shot-noise processes and applications to neuronal membranes

Marco Brigham, Alain Destexhe
2015 Physical Review E  
Filtered shot noise processes have proven to be very effective in modelling the evolution of systems exposed to stochastic shot noise sources, and have been applied to a wide variety of fields ranging  ...  In particular, they can model the membrane potential Vm of neurons driven by stochastic input, where these filtered processes are able to capture the non-stationary characteristics of Vm fluctuations in  ...  MODEL OF FILTERED SHOT NOISE PROCESS In this section we present a simple model of filtered shot noise process with multiplicative noise.  ... 
doi:10.1103/physreve.91.062102 pmid:26172656 fatcat:kox3asjl3fclpbqwvg2i6bolx4

Variational Algorithms to Remove Stationary Noise: Applications to Microscopy Imaging

J. Fehrenbach, P. Weiss, C. Lorenzo
2012 IEEE Transactions on Image Processing  
A framework and an algorithm are presented in order to remove stationary noise from images. This algorithm is called VSNR (Variational Stationary Noise Remover).  ...  It can be interpreted both as a restoration method in a Bayesian framework and as a cartoon+texture decomposition method.  ...  ACKNOWLEDGMENT The authors would like to thank Bernard Ducommun, Raphaël Jorand and Valérie Lobjois from the IP3D team in Toulouse cancéropole for their tireless support during this work and for all SPIM  ... 
doi:10.1109/tip.2012.2206037 pmid:22752131 fatcat:p3eeu3et75ez7abuou44tnwleq

Speech detection in non-stationary noise based on the 1/f process

Fan Wang, Fang Zheng, Wenhu Wu
2002 Journal of Computer Science and Technology  
Multiple templates are trained for the speech signal, and the parameters of the background noise can be dynamically adapted in runtime to model the variation of both the speech and the noise.  ...  The Gaussian 1/f process, a mathematical model for statistically self-similar random processes based on fractals, is selected to model both the speech and the background noise.  ...  For accuracy and robustness, multiple templates for speech are trained using a clustering process and the parameters of the background noise can be dynamically adapted in runtime to model the variation  ... 
doi:10.1007/bf02949828 fatcat:2rafcg34kvbfxajhc4xaov2lry

Stationary and non-stationary noise removal from cardiac signals using a Constrained Stability Least Mean Square algorithm

Mohammad Zia-Ur-Rahman, D V Rama Koti Reddy, Y. Sangeetha
2011 2011 International Conference on Communications and Signal Processing  
The adaptive filter essentially minimizes the mean-squared error between a primary input, which is the noisy ECG, and a reference input, which is either noise that is correlated in some way with the noise  ...  The results show that the performance of the CSLMS based algorithm is superior to that of the LMS based algorithm in noise reduction.  ...  This algorithm takes into account variation in the signal level at the filter output and selecting the normalized step size parameter that results in a stable as well as fast converging algorithm.  ... 
doi:10.1109/iccsp.2011.5739366 fatcat:6qamt2jsizgu3o3sptumgrwyki

Stationary Signal Processing on Graphs

Nathanael Perraudin, Pierre Vandergheynst
2017 IEEE Transactions on Signal Processing  
Graphs are a central tool in machine learning and information processing as they allow to conveniently capture the structure of complex datasets.  ...  In this context, it is of high importance to develop flexible models of signals defined over graphs or networks.  ...  This work has been supported by the Swiss National Science Foundation research project Towards Signal Processing on Graphs, grant number: 2000_21/154350/1.  ... 
doi:10.1109/tsp.2017.2690388 fatcat:66biiwl65za7tdrip5j7rq3npu

Hybrid modeling of non-stationary process variations

Eva Dyer, Mehrdad Majzoobi, Farinaz Koushanfar
2011 Proceedings of the 48th Design Automation Conference on - DAC '11  
To estimate the parameters in our hybrid spatial model, we develop a host of techniques to both estimate the change-points in the random field and to find an appropriate partitioning of the chip into disjoint  ...  In order to provide a compact model for nonstationary process variations, we introduce a new hybrid spatial modeling framework that models the spatially varying random field as a union of non-overlapping  ...  CONCLUSIONS In this paper, we presented a set of novel methods for spatial modeling of non-stationary process variations.  ... 
doi:10.1145/2024724.2024768 dblp:conf/dac/DyerMK11 fatcat:coou45ntk5ex7angvw55hskei4

Removing non-stationary noise in spectrum sensing using matrix factorization

Jan-Willem van Bloem, Roel Schiphorst, Cornelis H Slump
2013 EURASIP Journal on Advances in Signal Processing  
This means that the obtained data is distorted with noise and imperfections from the analog front-end.  ...  In this article it is shown that the occupancy in the industrial, scientific and medical (ISM) band, obtained by using energy detection (ITU recommended threshold), can be an overestimation of spectrum  ...  The proposed method for removal of non-stationary AGC noise with the aid of matrix factorization is presented in Section 6.  ... 
doi:10.1186/1687-6180-2013-72 fatcat:mnw67ihpgraxdgz4ao2pbfppg4

Stochastically modeling multiscale stationary biological processes

Michael A. Rowland, Michael L. Mayo, Edward J. Perkins, Natàlia Garcia-Reyero, Attila Csikász-Nagy
2019 PLoS ONE  
We use a new approach to modeling multiscale stationary biological processes that embraces the noise found in experimental data to provide estimates of the parameter uncertainties and the potential mis-specification  ...  As a result, they are computationally difficult to model and current approaches are notoriously slow and computationally intensive (multiscale stochastic models), fail to capture the effects of noise across  ...  These considerations have spurred further research into new methods of parameter estimation and model development [16] [17] [18] .  ... 
doi:10.1371/journal.pone.0226687 pmid:31877201 pmcid:PMC6932771 fatcat:lrf7nq4lgbclhnoen5cjp2vnha

Stationary time-vertex signal processing [article]

Andreas Loukas, Nathanaël Perraudin
2019 arXiv   pre-print
In particular, for any jointly stationary process (a) one reliably learns the covariance structure from as little as a single realization of the process, and (b) solves MMSE recovery problems, such as  ...  known, or the process is not strictly stationary.  ...  ACKNOWLEDGMENT This work has been supported by the Swiss National Science Foundation research project Towards Signal Processing on Graphs (grant number: 2000 21/154350/1). APPENDIX A.  ... 
arXiv:1611.00255v3 fatcat:plqngybmnvcpjjszjt2nwij42u

Fourier Analysis of Stationary Processes [chapter]

David R. Brillinger
2011 Selected Works of David Brillinger  
SuchpmnIizationa De of i n _ t>eaa.e of euttelIt wod: in the 6eIdI of pldme ptO<:eM-ioI mel poIIe-code modulation.  ...  a dtacription of lOme of the impcw' bDt procedures of the Fourler mal>'* of ftlIkoaIDed ItatioDay dilcrete lime ...... lb_ proceduresinclDde the tIItimatIon of the paws spectrum, the flttlna of flDlW parameter  ...  STATIONARY POINT PROCESSES A variety of problems, such as tltose of traffic systems , queues, nerve pulses , shot noise, impulse noise, and microscopic theory of gases lead us to data tltat has tlte character  ... 
doi:10.1007/978-1-4614-1344-8_13 fatcat:vcsushayq5g6fem5vw75x2y2fm

Fourier analysis of stationary processes

D.R. Brillinger
1974 Proceedings of the IEEE  
SuchpmnIizationa De of i n _ t>eaa.e of euttelIt wod: in the 6eIdI of pldme ptO<:eM-ioI mel poIIe-code modulation.  ...  a dtacription of lOme of the impcw' bDt procedures of the Fourler mal>'* of ftlIkoaIDed ItatioDay dilcrete lime ...... lb_ proceduresinclDde the tIItimatIon of the paws spectrum, the flttlna of flDlW parameter  ...  STATIONARY POINT PROCESSES A variety of problems, such as tltose of traffic systems , queues, nerve pulses , shot noise, impulse noise, and microscopic theory of gases lead us to data tltat has tlte character  ... 
doi:10.1109/proc.1974.9682 fatcat:emmytp5i4jfprj5o2xpgjjne4e

Stationary time-vertex signal processing

Andreas Loukas, Nathanaël Perraudin
2019 EURASIP Journal on Advances in Signal Processing  
In particular, for any jointly stationary process (a) one reliably learns the covariance structure from as little as a single realization of the process and (b) solves MMSE recovery problems, such as interpolation  ...  known, or the process is not strictly stationary.  ...  Authors' contributions The two authors contributed equally both for the experiments and for the writing of the paper. Both authors read and approved the final manuscript. Funding  ... 
doi:10.1186/s13634-019-0631-7 pmid:31983922 pmcid:PMC6951473 fatcat:jouu2o5mcndurd2kdg7iwm63jy
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