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blind source separation

Blind source separation (BSS) is a signal processing technique that aims to separate multiple source signals from a set of mixed signals, without any prior knowledge about the sources or the mixing process. The goal is to recover the original source signals from the observed mixtures, typically using statistical and computational methods. BSS has applications in various fields such as audio signal processing, image processing, and telecommunications. It is used to extract useful information from mixed signals and to improve the quality of the source signals.

Papers

Showing 191200 of 211 papers

TitleStatusHype
Difficulties applying recent blind source separation techniques to EEG and MEG0
Sparsity and adaptivity for the blind separation of partially correlated sourcesCode0
Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation0
Estimating the intrinsic dimension in fMRI space via dataset fractal analysis - Counting the `cpu cores' of the human brain0
A RobustICA Based Algorithm for Blind Separation of Convolutive Mixtures0
NMF with Sparse Regularizations in Transformed DomainsCode0
Kernel Nonnegative Matrix Factorization Without the Curse of the Pre-image - Application to Unmixing Hyperspectral Images0
Convex Analysis of Mixtures for Separating Non-negative Well-grounded Sources0
Generalized Canonical Correlation Analysis and Its Application to Blind Source Separation Based on a Dual-Linear Predictor Structure0
Phase transitions and sample complexity in Bayes-optimal matrix factorization0
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