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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 161170 of 211 papers

TitleStatusHype
Blind Source Separation with Optimal Transport Non-negative Matrix Factorization0
Multiple component decomposition from millimeter single-channel data0
Blind Source Separation Using Mixtures of Alpha-Stable DistributionsCode0
Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI Big Data Analytics0
Elliptical modeling and pattern analysis for perturbation models and classfication0
A unified method for super-resolution recovery and real exponential-sum separation0
A probabilistic model for learning in cortical microcircuit motifs with data-based divisive inhibition0
Blind nonnegative source separation using biological neural networks0
Discovery and visualization of structural biomarkers from MRI using transport-based morphometry0
Inertia-Constrained Pixel-by-Pixel Nonnegative Matrix Factorisation: a Hyperspectral Unmixing Method Dealing with Intra-class Variability0
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