| ivaBSS-package {ivaBSS} | R Documentation |
Tools for Independent Vector Analysis
Description
Independent vector analysis (IVA) is a blind source separation (BSS) model where several datasets are jointly unmixed. This package provides several methods for the unmixing together with some performance measures. For details, see Anderson et al. (2011) <doi:10.1109/TSP.2011.2181836> and Lee et al. (2007) <doi:10.1016/j.sigpro.2007.01.010>.
Details
The package contains tools for independent vector analysis. The main functions to perform IVA are "IVANewton" and "fastIVA". "NewtonIVA" performs Newton update based IVA and "fastIVA" performs fixed-point iteration based IVA. Both of the algorithms have multiple options for source density models.
Author(s)
Authors: Mika Sipilä, Klaus Nordhausen, Sara Taskinen
Maintainer: Mika Sipilä
References
Anderson, M., Adalı, T., & Li, X.-L. (2011). Joint blind source separation with multivariate Gaussian model: Algorithms and performance analysis. IEEE Transactions on Signal Processing, 60, 1672–1683. <doi:10.1109/TSP.2011.2181836>
Anderson, M. (2013). Independent vector analysis: Theory, algorithms, and applications. PhD dissertation, University of Maryland, Baltimore County.