blind source separation
44 papers with code • 0 benchmarks • 0 datasets
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.
Benchmarks
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Libraries
Use these libraries to find blind source separation models and implementationsMost implemented papers
Multidataset Independent Subspace Analysis with Application to Multimodal Fusion
In the last two decades, unsupervised latent variable models---blind source separation (BSS) especially---have enjoyed a strong reputation for the interpretable features they produce.
Deep Audio Prior
We are interested in applying deep networks in the absence of training dataset.
Blind Bounded Source Separation Using Neural Networks with Local Learning Rules
An important problem encountered by both natural and engineered signal processing systems is blind source separation.
Target Speech Extraction Based on Blind Source Separation and X-vector-based Speaker Selection Trained with Data Augmentation
Extracting the desired speech from a mixture is a meaningful and challenging task.
Sparse Separable Nonnegative Matrix Factorization
We propose a new variant of nonnegative matrix factorization (NMF), combining separability and sparsity assumptions.
Biologically plausible single-layer networks for nonnegative independent component analysis
To model how the brain performs this task, we seek a biologically plausible single-layer neural network implementation of a blind source separation algorithm.
Semi-Blind Source Separation for Nonlinear Acoustic Echo Cancellation
Unlike the commonly utilized adaptive algorithm, the proposed SBSS is based on the independence between the near-end signal and the reference signals, and is less sensitive to the mismatch of nonlinearity between the numerical and actual models.
Fetal ECG Extraction from Maternal ECG using Attention-based CycleGAN
Decomposing the FECG signal from maternal ECG (MECG) is a blind source separation problem, which is hard due to the low amplitude of FECG, the overlap of R waves, and the potential exposure to noise from different sources.
Joint deconvolution and unsupervised source separation for data on the sphere
This becomes highly challenging when applied to large data sampled on the sphere such as those provided by wide-field observations in astrophysics, whose analysis requires the design of dedicated robust and yet effective algorithms.
Nonlinear Independent Component Analysis for Discrete-Time and Continuous-Time Signals
We study the classical problem of recovering a multidimensional source signal from observations of nonlinear mixtures of this signal.
