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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 101–150 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
Blind Source Separation for NMR Spectra with Negative Intensity—0
Application of independent component analysis and TOPSIS to deal with dependent criteria in multicriteria decision problems—0
A Unified Bayesian View on Spatially Informed Source Separation and Extraction based on Independent Vector Analysis—0
Spatially Informed Independent Vector Analysis—0
Deep Audio PriorCode0
Analysis Co-Sparse Coding for Energy Disaggregation—0
Deep Sparse Coding for Non-Intrusive Load Monitoring—0
Multidataset Independent Subspace Analysis with Application to Multimodal FusionCode0
Improved Differentially Private Decentralized Source Separation for fMRI Data—0
A Novel CMB Component Separation Method: Hierarchical Generalized Morphological Component Analysis—0
Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments—0
Hierarchical Probabilistic Model for Blind Source Separation via Legendre TransformationCode0
Joint, Partially-joint, and Individual Independent Component Analysis in Multi-Subject fMRI Data—0
S\'eparation de sources doublement non stationnaire—0
Learning gradient-based ICA by neurally estimating mutual information—0
Bayesian Non-Parametric Multi-Source Modelling Based Determined Blind Source Separation—0
Unsupervised training of a deep clustering model for multichannel blind source separation—0
Time Series Source Separation using Dynamic Mode DecompositionCode0
Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data—0
Heuristics for Efficient Sparse Blind Source Separation—0
Towards Unsupervised Single-Channel Blind Source Separation using Adversarial Pair Unmix-and-Remix—0
Sparse component separation from Poisson measurements—0
Subtask Gated Networks for Non-Intrusive Load Monitoring—0
Referenceless Performance Evaluation of Audio Source Separation using Deep Neural Networks—0
CountNet: Estimating the Number of Concurrent Speakers Using Supervised Learning Speaker Count EstimationCode0
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