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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

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Showing 161–170 of 211 papers

TitleStatusHype
A deep learning pipeline for identification of motor units in musculoskeletal ultrasound—0
A Deep Learning Technique using Low Sampling rate for residential Non Intrusive Load Monitoring—0
A Hypothesis Testing Approach to Nonstationary Source Separation—0
Analysis Co-Sparse Coding for Energy Disaggregation—0
A Neural Network for Determination of Latent Dimensionality in Nonnegative Matrix Factorization—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
An Exploration of Optimal Parameters for Efficient Blind Source Separation of EEG Recordings Using AMICA—0
A Normative and Biologically Plausible Algorithm for Independent Component Analysis—0
A Novel CMB Component Separation Method: Hierarchical Generalized Morphological Component Analysis—0
An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks—0
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