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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 121130 of 211 papers

TitleStatusHype
Target Speech Extraction Based on Blind Source Separation and X-vector-based Speaker Selection Trained with Data AugmentationCode0
Determined BSS based on time-frequency masking and its application to harmonic vector analysis0
Blind Bounded Source Separation Using Neural Networks with Local Learning RulesCode0
Faster IVA: Update Rules for Independent Vector Analysis based on Negentropy and the Majorize-Minimize PrincipleCode1
Quaternion Non-negative Matrix Factorization: definition, uniqueness and algorithm0
Blind Source Separation for NMR Spectra with Negative Intensity0
Application of independent component analysis and TOPSIS to deal with dependent criteria in multicriteria decision problems0
A Unified Bayesian View on Spatially Informed Source Separation and Extraction based on Independent Vector Analysis0
Spatially Informed Independent Vector Analysis0
Deep Audio PriorCode0
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