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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 111120 of 211 papers

TitleStatusHype
Frequency domain TRINICON-based blind source separation method with multi-source activity detection for sparsely mixed signals0
Generalized Canonical Correlation Analysis and Its Application to Blind Source Separation Based on a Dual-Linear Predictor Structure0
Generalized Fast Multichannel Nonnegative Matrix Factorization Based on Gaussian Scale Mixtures for Blind Source Separation0
Generalized Non-orthogonal Joint Diagonalization with LU Decomposition and Successive Rotations0
Gradient of Probability Density Functions based Contrasts for Blind Source Separation (BSS)0
Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction0
Heuristics for Efficient Sparse Blind Source Separation0
High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning0
How secure is the time-modulated array-enabled ofdm directional modulation?0
HYPERION: Hyperspectral Penetrating-type Ellipsoidal Reconstruction for Terahertz Blind Source Separation0
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