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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 181190 of 211 papers

TitleStatusHype
Joint Sound Source Separation and Speaker Recognition0
Variational Autoencoders for Feature Detection of Magnetic Resonance Imaging Data0
Blind Source Separation: Fundamentals and Recent Advances (A Tutorial Overview Presented at SBrT-2001)0
Robust Heart Rate Measurement From Video Using Select Random Patches0
Latent Bayesian melding for integrating individual and population modelsCode0
Robust Sparse Blind Source Separation0
Blind Source Separation Algorithms Using Hyperbolic and Givens Rotations for High-Order QAM Constellations0
Gradient of Probability Density Functions based Contrasts for Blind Source Separation (BSS)0
Non-parametric Bayesian Models of Response Function in Dynamic Image Sequences0
Convergent Bayesian formulations of blind source separation and electromagnetic source estimation0
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