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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 91100 of 211 papers

TitleStatusHype
Independent Component Analysis by Entropy Maximization with Kernels0
Blind Source Separation for NMR Spectra with Negative Intensity0
Independent Vector Analysis via Log-Quadratically Penalized Quadratic Minimization0
Independent Vector Extraction for Fast Joint Blind Source Separation and Dereverberation0
Electrode Selection for Noninvasive Fetal Electrocardiogram Extraction using Mutual Information Criteria0
Integration of deep learning with expectation maximization for spatial cue based speech separation in reverberant conditions0
Inverse-free Online Independent Vector Analysis with Flexible Iterative Source Steering0
Joint deconvolution and blind source separation on the sphere with an application to radio-astronomy0
Deep Sparse Coding for Non-Intrusive Load Monitoring0
Effective Blind Source Separation Based on the Adam Algorithm0
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