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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 201211 of 211 papers

TitleStatusHype
Dialogue Enhancement in Object-based Audio -- Evaluating the Benefit on People above 650
Difficulties applying recent blind source separation techniques to EEG and MEG0
Direction Finding in Partly Calibrated Arrays Exploiting the Whole Array Aperture0
Discovery and visualization of structural biomarkers from MRI using transport-based morphometry0
Distributed Blind Source Separation based on FastICA0
DNN-Free Low-Latency Adaptive Speech Enhancement Based on Frame-Online Beamforming Powered by Block-Online FastMNMF0
Double Coupled Canonical Polyadic Decomposition for Joint Blind Source Separation0
EchoVest: Real-Time Sound Classification and Depth Perception Expressed through Transcutaneous Electrical Nerve Stimulation0
Effective Blind Source Separation Based on the Adam Algorithm0
Electrode Selection for Noninvasive Fetal Electrocardiogram Extraction using Mutual Information Criteria0
Elliptical modeling and pattern analysis for perturbation models and classfication0
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