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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 26–50 of 211 papers

TitleStatusHype
Blind Source Separation of Single-Channel Mixtures via Multi-Encoder AutoencodersCode1
EOG Artifact Removal from Single and Multi-channel EEG Recordings through the combination of Long Short-Term Memory Networks and Independent Component Analysis—0
EchoVest: Real-Time Sound Classification and Depth Perception Expressed through Transcutaneous Electrical Nerve Stimulation—0
Source Identification: A Self-Supervision Task for Dense Prediction—0
Ongoing EEG artifact correction using blind source separation—0
Neural Fast Full-Rank Spatial Covariance Analysis for Blind Source Separation—0
AudioSlots: A slot-centric generative model for audio separation—0
A New Non-Negative Matrix Factorization Approach for Blind Source Separation of Cardiovascular and Respiratory Sound Based on the Periodicity of Heart and Lung Function—0
A Robustness Analysis of Blind Source Separation—0
MSDC: Exploiting Multi-State Power Consumption in Non-intrusive Load Monitoring based on A Dual-CNN Model—0
Identification of Power System Oscillation Modes using Blind Source Separation based on Copula StatisticCode0
Electrode Selection for Noninvasive Fetal Electrocardiogram Extraction using Mutual Information Criteria—0
Nonparametric Independent Component Analysis for the Sources with Mixed Spectra—0
GPU-accelerated Guided Source Separation for Meeting TranscriptionCode1
Direction Finding in Partly Calibrated Arrays Exploiting the Whole Array Aperture—0
Transformer-based Hand Gesture Recognition via High-Density EMG Signals: From Instantaneous Recognition to Fusion of Motor Unit Spike TrainsCode1
A Framework to Evaluate Independent Component Analysis applied to EEG signal: testing on the Picard algorithmCode0
Multi-View Independent Component Analysis with Shared and Individual Sources—0
Semi-Blind Source Separation with Learned ConstraintsCode0
Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated SourcesCode0
Least-squares methods for nonnegative matrix factorization over rational functions—0
Large-Sample Properties of Non-Stationary Source Separation for Gaussian Signals—0
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis—0
Parameter Estimation of Mixed Gaussian-Impulsive Noise: An U-net++ Based Method—0
Inverse-free Online Independent Vector Analysis with Flexible Iterative Source Steering—0
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