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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 11–20 of 211 papers

TitleStatusHype
Enhancing Blind Source Separation with Dissociative Principal Component Analysis—0
Distributed Blind Source Separation based on FastICA—0
MotionLeaf: Fine-grained Multi-Leaf Damped Vibration Monitoring for Plant Water Stress using Low-Cost mmWave Sensors—0
Unsupervised Composable Representations for AudioCode1
PRIME: Blind Multispectral Unmixing Using Virtual Quantum Prism and Convex Geometry—0
S^3 -- Semantic Signal SeparationCode2
Neural Blind Source Separation and Diarization for Distant Speech Recognition—0
Nonparametric Evaluation of Noisy ICA Solutions—0
Online Similarity-and-Independence-Aware Beamformer for Low-latency Target Sound Extraction—0
A computationally efficient semi-blind source separation based approach for nonlinear echo cancellation based on an element-wise iterative source steering—0
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