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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
Dialogue Enhancement in Object-based Audio -- Evaluating the Benefit on People above 65—0
Difficulties applying recent blind source separation techniques to EEG and MEG—0
Blind source separation of baseband RF communication signals using mixed-signal matrix multiplication circuit—0
A New Non-Negative Matrix Factorization Approach for Blind Source Separation of Cardiovascular and Respiratory Sound Based on the Periodicity of Heart and Lung Function—0
Direction Finding in Partly Calibrated Arrays Exploiting the Whole Array Aperture—0
Discovery and visualization of structural biomarkers from MRI using transport-based morphometry—0
Distributed Blind Source Separation based on FastICA—0
DNN-Free Low-Latency Adaptive Speech Enhancement Based on Frame-Online Beamforming Powered by Block-Online FastMNMF—0
Double Coupled Canonical Polyadic Decomposition for Joint Blind Source Separation—0
A deep learning pipeline for identification of motor units in musculoskeletal ultrasound—0
Blind Source Separation Algorithms Using Hyperbolic and Givens Rotations for High-Order QAM Constellations—0
EchoVest: Real-Time Sound Classification and Depth Perception Expressed through Transcutaneous Electrical Nerve Stimulation—0
Blind Source Separation in Biomedical Signals Using Variational Methods—0
EOG Artifact Removal from Single and Multi-channel EEG Recordings through the combination of Long Short-Term Memory Networks and Independent Component Analysis—0
Enhancing ICA Performance by Exploiting Sparsity: Application to FMRI Analysis—0
Blind Source Separation: Fundamentals and Recent Advances (A Tutorial Overview Presented at SBrT-2001)—0
A Robustness Analysis of Blind Source Separation—0
Enhancing Blind Source Separation with Dissociative Principal Component Analysis—0
Estimating Sparse Sources from Data Mixtures using Maxima in Phase Space Plots—0
Estimating the intrinsic dimension in fMRI space via dataset fractal analysis - Counting the `cpu cores' of the human brain—0
Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI Big Data Analytics—0
Elliptical modeling and pattern analysis for perturbation models and classfication—0
Blind Source Separation for NMR Spectra with Negative Intensity—0
Frequency domain TRINICON-based blind source separation method with multi-source activity detection for sparsely mixed signals—0
Electrode Selection for Noninvasive Fetal Electrocardiogram Extraction using Mutual Information Criteria—0
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