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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 101–125 of 211 papers

TitleStatusHype
Joint deconvolution and unsupervised source separation for data on the sphereCode0
Provably robust blind source separation of linear-quadratic near-separable mixtures—0
Deep-RLS: A Model-Inspired Deep Learning Approach to Nonlinear PCA—0
Singular Sturm-Liouville Problems with Zero Potential (q=0) and Singular Slow Feature Analysis—0
Semi-Blind Source Separation for Nonlinear Acoustic Echo CancellationCode0
Biologically plausible single-layer networks for nonnegative independent component analysisCode0
A deep learning pipeline for identification of motor units in musculoskeletal ultrasound—0
Joint deconvolution and blind source separation on the sphere with an application to radio-astronomy—0
Lorentzian Peak Sharpening and Sparse Blind Source Separation for NMR Spectroscopy—0
Independent Vector Analysis via Log-Quadratically Penalized Quadratic Minimization—0
LOCUS: A Novel Decomposition Method for Brain Network Connectivity Matrices using Low-rank Structure with Uniform Sparsity—0
Respiratory Sound Classification Using Long-Short Term Memory—0
Time Series Source Separation with Slow Flows—0
Multi-Resolution Beta-Divergence NMF for Blind Spectral Unmixing—0
On Cokriging, Neural Networks, and Spatial Blind Source Separation for Multivariate Spatial Prediction—0
Dialogue Enhancement in Object-based Audio -- Evaluating the Benefit on People above 65—0
A Neural Network for Determination of Latent Dimensionality in Nonnegative Matrix Factorization—0
Controlling for sparsity in sparse factor analysis models: adaptive latent feature sharing for piecewise linear dimensionality reduction—0
Sparse Separable Nonnegative Matrix FactorizationCode0
Sequence to Point Learning Based on Bidirectional Dilated Residual Network for Non Intrusive Load Monitoring—0
Consistent ICA: Determined BSS meets spectrogram consistency—0
Target Speech Extraction Based on Blind Source Separation and X-vector-based Speaker Selection Trained with Data AugmentationCode0
Determined BSS based on time-frequency masking and its application to harmonic vector analysis—0
Blind Bounded Source Separation Using Neural Networks with Local Learning RulesCode0
Quaternion Non-negative Matrix Factorization: definition, uniqueness and algorithm—0
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