SOTAVerified

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 6170 of 211 papers

TitleStatusHype
Blind Source Separation Algorithms Using Hyperbolic and Givens Rotations for High-Order QAM Constellations0
EchoVest: Real-Time Sound Classification and Depth Perception Expressed through Transcutaneous Electrical Nerve Stimulation0
Enhancing ICA Performance by Exploiting Sparsity: Application to FMRI Analysis0
Blind Source Separation: Fundamentals and Recent Advances (A Tutorial Overview Presented at SBrT-2001)0
A Robustness Analysis of Blind Source Separation0
Enhancing Blind Source Separation with Dissociative Principal Component Analysis0
Elliptical modeling and pattern analysis for perturbation models and classfication0
EOG Artifact Removal from Single and Multi-channel EEG Recordings through the combination of Long Short-Term Memory Networks and Independent Component Analysis0
Estimating Sparse Sources from Data Mixtures using Maxima in Phase Space Plots0
Blind Source Separation for NMR Spectra with Negative Intensity0
Show:102550
← PrevPage 7 of 22Next →

No leaderboard results yet.