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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 5160 of 211 papers

TitleStatusHype
Estimating Sparse Sources from Data Mixtures using Maxima in Phase Space Plots0
Implementation of fast ICA using memristor crossbar arrays for blind image source separations0
A Unifying View on Blind Source Separation of Convolutive Mixtures based on Independent Component Analysis0
DNN-Free Low-Latency Adaptive Speech Enhancement Based on Frame-Online Beamforming Powered by Block-Online FastMNMF0
Direction-Aware Adaptive Online Neural Speech Enhancement with an Augmented Reality Headset in Real Noisy Conversational EnvironmentsCode2
Semi-blind source separation using convolutive transfer function for nonlinear acoustic echo cancellationCode0
Speech Artifact Removal from EEG Recordings of Spoken Word Production with Tensor Decomposition0
Generalized Fast Multichannel Nonnegative Matrix Factorization Based on Gaussian Scale Mixtures for Blind Source Separation0
Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation0
Target Confusion in End-to-end Speaker Extraction: Analysis and Approaches0
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