Search results for "second order source separation"

showing 2 items of 2 documents

Blind Source Separation Based on Joint Diagonalization in R: The Packages JADE and BSSasymp

2017

Blind source separation (BSS) is a well-known signal processing tool which is used to solve practical data analysis problems in various fields of science. In BSS, we assume that the observed data consists of linear mixtures of latent variables. The mixing system and the distributions of the latent variables are unknown. The aim is to find an estimate of an unmixing matrix which then transforms the observed data back to latent sources. In this paper we present the R packages JADE and BSSasymp. The package JADE offers several BSS methods which are based on joint diagonalization. Package BSSasymp contains functions for computing the asymptotic covariance matrices as well as their data-based es…

Statistics and ProbabilityComputer scienceJADE (programming language)02 engineering and technologyLatent variableMachine learningcomputer.software_genre01 natural sciencesBlind signal separation010104 statistics & probabilityMatrix (mathematics)nonstationary source separationMixing (mathematics)0202 electrical engineering electronic engineering information engineeringsecond order source separation0101 mathematicslcsh:Statisticslcsh:HA1-4737computer.programming_languageta113Signal processingta112matematiikkamultivariate time seriesmathematicsbusiness.industryEstimator020206 networking & telecommunicationsriippumattomien komponenttien analyysiindependent component analysis; multivariate time series; nonstationary source separation; performance indices; second order source separationIndependent component analysisperformance indicesstatisticsindependent component analysisArtificial intelligenceStatistics Probability and UncertaintybusinesscomputerAlgorithmSoftwareJournal of Statistical Software
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Signal dimension estimation in BSS models with serial dependence

2022

Many modern multivariate time series datasets contain a large amount of noise, and the first step of the data analysis is to separate the noise channels from the signals of interest. A crucial part of this dimension reduction is determining the number of signals. In this paper we approach this problem by considering a noisy latent variable time series model which comprises many popular blind source separation models. We propose a general framework for the estimation of the signal dimension that is based on testing for sub-sphericity and give examples of different tests suitable for time series settings. In the inference we rely on bootstrap null distributions. Several simulation studies are…

nonstationary source separationdimension reductionsignaalinkäsittelyaikasarjatsub-sphericitysecond order source separationblock bootstrapaikasarja-analyysi2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
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