# Blind Source Separation and Independent Component Analysis : A Review

@inproceedings{Choi2004BlindSS, title={Blind Source Separation and Independent Component Analysis : A Review}, author={Seungjin Choi}, year={2004} }

Blind source separation (BSS) and independent component analysis (ICA) are generally based on a wide class of unsupervised learning algorithms and they found potential applications in many areas from engineering to neuroscience. A recent trend in BSS is to consider problems in the framework of matrix factorization or more general signals decomposition with probabilistic generative and tree structured graphical models and exploit a priori knowledge about true nature and structure of latent…

## 318 Citations

### Sparsity and Adaptivity for the Blind Separation of Partially Correlated Sources

- Computer ScienceIEEE Transactions on Signal Processing
- 2015

This paper introduces a novel sparsity-enforcing BSS method coined Adaptive Morphological Component Analysis (AMCA), which is designed to retrieve sparse and partially correlated sources and makes profit of an adaptive re-weighting scheme to favor/penalize samples based on their level of correlation.

### A Blind Source Separation of instantaneous acoustic mixtures using Natural Gradient Method

- Computer Science2012 IEEE International Conference on Control System, Computing and Engineering
- 2012

A great weight has been given on an excellent rendition of the Infomax technique of Independent Component Analysis, called the Natural Gradient Method, to employ a cost function that would yield an optimized de-mixing matrix, producing fairly estimated source signals.

### A convex model and L1 minimization for musical noise reduction in blind source separation

- Computer Science
- 2012

An efficient musical noise reduction method is presented based on a convex model of time-domain sparse filters that can be used as a post-processing tool for more general and recent versions of TF domain BSS methods as well.

### Sparse blind source separation for partially correlated sources

- Computer Science2014 IEEE International Conference on Image Processing (ICIP)
- 2014

A new sparsity-enforcing BSS method coined Adaptive Morphological Component Analysis (AMCA) designed to retrieve sparse and partially correlated sources based on an adaptive weighting scheme is presented.

### Anechoic Blind Source Separation Using Wigner Marginals

- Computer ScienceJ. Mach. Learn. Res.
- 2011

A new algorithmic framework for the solution of anechoic problems in arbitrary dimensions is presented, derived from stochastic time-frequency analysis in general and the marginal properties of the Wigner-Ville spectrum in particular.

### Postprocessing and sparse blind source separation of positive and partially overlapped data

- Computer ScienceSignal Process.
- 2011

### A geometric blind source separation method based on facet component analysis

- Computer ScienceSignal Image Video Process.
- 2016

This work presents a geometric approach for blind separation of nonnegative linear mixtures termed facet component analysis based on facet identification of the underlying cone structure of the data, and develops an efficient facet identification method by combining data classification and linear regression.

### MINIMIZATION FOR MUSICAL NOISE REDUCTION IN BLIND SOURCE SEPARATION

- Computer Science
- 2011

An efficient musical noise reduction method is presented based on a convex model of time-domain sparse filters that can be used as a post-processing tool for more general and recent versions of TF domain BSS methods as well.

### Efficient separation of image mixtures

- Computer Science
- 2007

This paper will try to show the efficacity of several ICA algorithms starting from basic image types and work a way up to real-life situations, as is the case with the reflections.

### A Linear Source Recovery Method for Underdetermined Mixtures of Uncorrelated AR-Model Signals Without Sparseness

- Computer ScienceIEEE Transactions on Signal Processing
- 2014

This work proves that the UBSS problem can be converted into a determined problem by combining the source AR model together with the original mixing equation to form a state-space model and applies the Kalman filter to obtain a linear source estimate in the minimum mean-squared error sense.

## References

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We introduce a new extended model for independent component analysis (ICA) and/or blind source separation (BSS), in which the assumption of the standard ICA model that the source signals are mutually…

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