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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 7180 of 211 papers

TitleStatusHype
Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI Big Data Analytics0
Enhancing Blind Source Separation with Dissociative Principal Component Analysis0
Elliptical modeling and pattern analysis for perturbation models and classfication0
Frequency domain TRINICON-based blind source separation method with multi-source activity detection for sparsely mixed signals0
Blind Source Separation with Optimal Transport Non-negative Matrix Factorization0
Generalized Canonical Correlation Analysis and Its Application to Blind Source Separation Based on a Dual-Linear Predictor Structure0
Generalized Fast Multichannel Nonnegative Matrix Factorization Based on Gaussian Scale Mixtures for Blind Source Separation0
Generalized Non-orthogonal Joint Diagonalization with LU Decomposition and Successive Rotations0
Blind Source Separation for NMR Spectra with Negative Intensity0
Electrode Selection for Noninvasive Fetal Electrocardiogram Extraction using Mutual Information Criteria0
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