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Processing stationary noise: model and parameter selection in variational methods
[article]
2013
arXiv
pre-print
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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
2014
SIAM Journal of Imaging Sciences
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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
2018
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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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
2015
Physical Review E
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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
2012
IEEE Transactions on Image Processing
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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
2002
Journal of Computer Science and Technology
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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
2011
2011 International Conference on Communications and Signal Processing
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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
2017
IEEE Transactions on Signal Processing
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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
2011
Proceedings of the 48th Design Automation Conference on - DAC '11
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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
2013
EURASIP Journal on Advances in Signal Processing
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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
2019
PLoS ONE
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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]
2019
arXiv
pre-print
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2017 pre-print arXiv:1611.00255v2 fatcat:ylbyepxrkrfy3g6mdfiudgskg4Other Versions
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]
2011
Selected Works of David Brillinger
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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
1974
Proceedings of the IEEE
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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
2019
EURASIP Journal on Advances in Signal Processing
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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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