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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

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Showing 126–150 of 211 papers

TitleStatusHype
Independent Vector Extraction for Fast Joint Blind Source Separation and Dereverberation—0
Inertia-Constrained Pixel-by-Pixel Nonnegative Matrix Factorisation: a Hyperspectral Unmixing Method Dealing with Intra-class Variability—0
Integration of deep learning with expectation maximization for spatial cue based speech separation in reverberant conditions—0
Inverse-free Online Independent Vector Analysis with Flexible Iterative Source Steering—0
Joint deconvolution and blind source separation on the sphere with an application to radio-astronomy—0
Joint Dereverberation and Separation with Iterative Source Steering—0
Joint, Partially-joint, and Individual Independent Component Analysis in Multi-Subject fMRI Data—0
Joint Sound Source Separation and Speaker Recognition—0
Joint Spectrogram Separation and TDOA Estimation using Optimal Transport—0
Nonparametric Evaluation of Noisy ICA Solutions—0
Kernel Nonnegative Matrix Factorization Without the Curse of the Pre-image - Application to Unmixing Hyperspectral Images—0
Quantifying Non-linear Dependencies in Blind Source Separation of Power System Signals using Copula Statistics—0
Large-Sample Properties of Non-Stationary Source Separation for Gaussian Signals—0
Controlling for sparsity in sparse factor analysis models: adaptive latent feature sharing for piecewise linear dimensionality reduction—0
Learning gradient-based ICA by neurally estimating mutual information—0
Least-squares methods for nonnegative matrix factorization over rational functions—0
Linearly constrained Bayesian matrix factorization for blind source separation—0
LOCUS: A Novel Decomposition Method for Brain Network Connectivity Matrices using Low-rank Structure with Uniform Sparsity—0
Lorentzian Peak Sharpening and Sparse Blind Source Separation for NMR Spectroscopy—0
Low-Rank Matrix Factorizations with Volume-based Constraints and Regularizations—0
Machine learning methods for modelling and analysis of time series signals in geoinformatics—0
Modifications of FastICA in Convolutive Blind Source Separation—0
Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data—0
More Behind Your Electricity Bill: a Dual-DNN Approach to Non-Intrusive Load Monitoring—0
MotionLeaf: Fine-grained Multi-Leaf Damped Vibration Monitoring for Plant Water Stress using Low-Cost mmWave Sensors—0
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