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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 6170 of 211 papers

TitleStatusHype
Blind Demixing of Diffused Graph Signals0
EchoVest: Real-Time Sound Classification and Depth Perception Expressed through Transcutaneous Electrical Nerve Stimulation0
Application of independent component analysis and TOPSIS to deal with dependent criteria in multicriteria decision problems0
Analysis Co-Sparse Coding for Energy Disaggregation0
Difficulties applying recent blind source separation techniques to EEG and MEG0
Dialogue Enhancement in Object-based Audio -- Evaluating the Benefit on People above 650
An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks0
Determined BSS based on time-frequency masking and its application to harmonic vector analysis0
Deep Sparse Coding for Non-Intrusive Load Monitoring0
Deep-RLS: A Model-Inspired Deep Learning Approach to Nonlinear PCA0
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