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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 131140 of 211 papers

TitleStatusHype
Joint Dereverberation and Separation with Iterative Source Steering0
Joint, Partially-joint, and Individual Independent Component Analysis in Multi-Subject fMRI Data0
Joint Sound Source Separation and Speaker Recognition0
Joint Spectrogram Separation and TDOA Estimation using Optimal Transport0
Nonparametric Evaluation of Noisy ICA Solutions0
Kernel Nonnegative Matrix Factorization Without the Curse of the Pre-image - Application to Unmixing Hyperspectral Images0
Quantifying Non-linear Dependencies in Blind Source Separation of Power System Signals using Copula Statistics0
Large-Sample Properties of Non-Stationary Source Separation for Gaussian Signals0
Controlling for sparsity in sparse factor analysis models: adaptive latent feature sharing for piecewise linear dimensionality reduction0
Learning gradient-based ICA by neurally estimating mutual information0
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