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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 31–40 of 211 papers

TitleStatusHype
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
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