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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
Biologically plausible single-layer networks for nonnegative independent component analysisCode0
Sparse Pursuit and Dictionary Learning for Blind Source Separation in Polyphonic Music RecordingsCode0
Blind Source Separation Algorithms Using Hyperbolic and Givens Rotations for High-Order QAM Constellations—0
Double Coupled Canonical Polyadic Decomposition for Joint Blind Source Separation—0
A probabilistic model for learning in cortical microcircuit motifs with data-based divisive inhibition—0
DNN-Free Low-Latency Adaptive Speech Enhancement Based on Frame-Online Beamforming Powered by Block-Online FastMNMF—0
Distributed Blind Source Separation based on FastICA—0
Blind nonnegative source separation using biological neural networks—0
Discovery and visualization of structural biomarkers from MRI using transport-based morphometry—0
Direction Finding in Partly Calibrated Arrays Exploiting the Whole Array Aperture—0
Blind Demixing of Diffused Graph Signals—0
EchoVest: Real-Time Sound Classification and Depth Perception Expressed through Transcutaneous Electrical Nerve Stimulation—0
Application of independent component analysis and TOPSIS to deal with dependent criteria in multicriteria decision problems—0
Analysis Co-Sparse Coding for Energy Disaggregation—0
Difficulties applying recent blind source separation techniques to EEG and MEG—0
Dialogue Enhancement in Object-based Audio -- Evaluating the Benefit on People above 65—0
An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks—0
Determined BSS based on time-frequency masking and its application to harmonic vector analysis—0
Deep Sparse Coding for Non-Intrusive Load Monitoring—0
Deep-RLS: A Model-Inspired Deep Learning Approach to Nonlinear PCA—0
Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments—0
A Novel CMB Component Separation Method: Hierarchical Generalized Morphological Component Analysis—0
A Deep Learning Technique using Low Sampling rate for residential Non Intrusive Load Monitoring—0
Data-Driven Source Separation Based on Simplex Analysis—0
Bayesian Non-Parametric Multi-Source Modelling Based Determined Blind Source Separation—0
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