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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 2650 of 211 papers

TitleStatusHype
Blind Bounded Source Separation Using Neural Networks with Local Learning RulesCode0
Scalable Convolutional Dictionary Learning with Constrained Recurrent Sparse Auto-encodersCode0
Enhancing Deep Learning Models through Tensorization: A Comprehensive Survey and FrameworkCode0
Target Speech Extraction Based on Blind Source Separation and X-vector-based Speaker Selection Trained with Data AugmentationCode0
Towards Reliable Objective Evaluation Metrics for Generative Singing Voice Separation ModelsCode0
Semi-Blind Source Separation for Nonlinear Acoustic Echo CancellationCode0
On the achievability of blind source separation for high-dimensional nonlinear source mixturesCode0
Sparse Pursuit and Dictionary Learning for Blind Source Separation in Polyphonic Music RecordingsCode0
Revisiting convolutive blind source separation for identifying spiking motor neuron activity: From theory to practiceCode0
Semi-blind source separation using convolutive transfer function for nonlinear acoustic echo cancellationCode0
Identification of Power System Oscillation Modes using Blind Source Separation based on Copula StatisticCode0
Hierarchical Probabilistic Model for Blind Source Separation via Legendre TransformationCode0
Blind Source Separation Using Mixtures of Alpha-Stable DistributionsCode0
Modeling the Repetition-based Recovering of Acoustic and Visual Sources with Dendritic NeuronsCode0
Multidataset Independent Subspace Analysis with Application to Multimodal FusionCode0
NMF with Sparse Regularizations in Transformed DomainsCode0
Nonlinear Independent Component Analysis for Discrete-Time and Continuous-Time SignalsCode0
Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image StatisticsCode0
CountNet: Estimating the Number of Concurrent Speakers Using Supervised Learning Speaker Count EstimationCode0
Joint deconvolution and unsupervised source separation for data on the sphereCode0
Deep Audio PriorCode0
DURRNet: Deep Unfolded Single Image Reflection Removal NetworkCode0
A Framework to Evaluate Independent Component Analysis applied to EEG signal: testing on the Picard algorithmCode0
Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated SourcesCode0
Latent Bayesian melding for integrating individual and population modelsCode0
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