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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
Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image StatisticsCode0
Generalized Non-orthogonal Joint Diagonalization with LU Decomposition and Successive Rotations0
Successive Nonnegative Projection Algorithm for Robust Nonnegative Blind Source Separation0
Sparse and Non-Negative BSS for Noisy DataCode0
Robust Pulse Rate From Chrominance-Based rPPG0
Tensor Decompositions: A New Concept in Brain Data Analysis?0
Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction0
Semi-blind Source Separation via Sparse Representations and Online Dictionary Learning0
基於稀疏成份分析之旋積盲訊號源分離方法 (Convolutive Blind Source Separation Based on Sparse Component Analysis) [In Chinese]0
Linearly constrained Bayesian matrix factorization for blind source separation0
The Infinite Factorial Hidden Markov Model0
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