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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 151–175 of 211 papers

TitleStatusHype
Towards Human Pulse Rate Estimation from Face Video: Automatic Component Selection and Comparison of Blind Source Separation Methods—0
Semi-blind source separation with multichannel variational autoencoderCode0
On the achievability of blind source separation for high-dimensional nonlinear source mixturesCode0
Scalable Convolutional Dictionary Learning with Constrained Recurrent Sparse Auto-encodersCode0
Signals as Parametric Curves: Application to Independent Component Analysis and Blind Source Separation—0
Sparse Pursuit and Dictionary Learning for Blind Source Separation in Polyphonic Music RecordingsCode0
An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks—0
Trace your sources in large-scale data: one ring to find them allCode0
Data-Driven Source Separation Based on Simplex Analysis—0
Frequency domain TRINICON-based blind source separation method with multi-source activity detection for sparsely mixed signals—0
Blind Source Separation with Optimal Transport Non-negative Matrix Factorization—0
Multiple component decomposition from millimeter single-channel data—0
Blind Source Separation Using Mixtures of Alpha-Stable DistributionsCode0
Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI Big Data Analytics—0
Elliptical modeling and pattern analysis for perturbation models and classfication—0
A unified method for super-resolution recovery and real exponential-sum separation—0
A probabilistic model for learning in cortical microcircuit motifs with data-based divisive inhibition—0
Blind nonnegative source separation using biological neural networks—0
Discovery and visualization of structural biomarkers from MRI using transport-based morphometry—0
Inertia-Constrained Pixel-by-Pixel Nonnegative Matrix Factorisation: a Hyperspectral Unmixing Method Dealing with Intra-class Variability—0
Strongly-Typed Agents are Guaranteed to Interact Safely—0
Double Coupled Canonical Polyadic Decomposition for Joint Blind Source Separation—0
Sequence-to-point learning with neural networks for nonintrusive load monitoringCode0
Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals—0
Spatio-temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks—0
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