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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
Joint deconvolution and unsupervised source separation for data on the sphereCode0
Target Speech Extraction Based on Blind Source Separation and X-vector-based Speaker Selection Trained with Data AugmentationCode0
NMF with Sparse Regularizations in Transformed DomainsCode0
Scalable Convolutional Dictionary Learning with Constrained Recurrent Sparse Auto-encodersCode0
Nonlinear Independent Component Analysis for Discrete-Time and Continuous-Time SignalsCode0
Self-Supervised Autoencoder Network for Robust Heart Rate Extraction from Noisy Photoplethysmogram: Applying Blind Source Separation to Biosignal AnalysisCode0
Blind Bounded Source Separation Using Neural Networks with Local Learning RulesCode0
Semi-Blind Source Separation for Nonlinear Acoustic Echo CancellationCode0
Semi-blind source separation using convolutive transfer function for nonlinear acoustic echo cancellationCode0
Latent Bayesian melding for integrating individual and population modelsCode0
Towards Reliable Objective Evaluation Metrics for Generative Singing Voice Separation ModelsCode0
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