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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 51–75 of 211 papers

TitleStatusHype
A Framework to Evaluate Independent Component Analysis applied to EEG signal: testing on the Picard algorithmCode0
Multi-View Independent Component Analysis with Shared and Individual Sources—0
Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated SourcesCode0
Semi-Blind Source Separation with Learned ConstraintsCode0
Least-squares methods for nonnegative matrix factorization over rational functions—0
Large-Sample Properties of Non-Stationary Source Separation for Gaussian Signals—0
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis—0
Parameter Estimation of Mixed Gaussian-Impulsive Noise: An U-net++ Based Method—0
Inverse-free Online Independent Vector Analysis with Flexible Iterative Source Steering—0
Estimating Sparse Sources from Data Mixtures using Maxima in Phase Space Plots—0
Implementation of fast ICA using memristor crossbar arrays for blind image source separations—0
A Unifying View on Blind Source Separation of Convolutive Mixtures based on Independent Component Analysis—0
DNN-Free Low-Latency Adaptive Speech Enhancement Based on Frame-Online Beamforming Powered by Block-Online FastMNMF—0
Semi-blind source separation using convolutive transfer function for nonlinear acoustic echo cancellationCode0
Speech Artifact Removal from EEG Recordings of Spoken Word Production with Tensor Decomposition—0
Generalized Fast Multichannel Nonnegative Matrix Factorization Based on Gaussian Scale Mixtures for Blind Source Separation—0
Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation—0
Target Confusion in End-to-end Speaker Extraction: Analysis and Approaches—0
Spatial Loss for Unsupervised Multi-channel Source Separation—0
Blind Source Separation for Mixture of Sinusoids with Near-Linear Computational Complexity—0
DURRNet: Deep Unfolded Single Image Reflection Removal NetworkCode0
Single microphone speaker extraction using unified time-frequency Siamese-Unet—0
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems—0
Blind source separation of baseband RF communication signals using mixed-signal matrix multiplication circuit—0
Modeling the Repetition-based Recovering of Acoustic and Visual Sources with Dendritic NeuronsCode0
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