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Stability of independent vector analysis
Signal Processing, 2012Independent vector analysis (IVA) is a method for solving the permutation problem that is inherent in the frequency-domain independent component analysis for convoluted mixtures. IVA utilizes inner dependency among the frequency components of each source.
Takashi Itahashi, Kiyotoshi Matsuoka
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Independent Vector Analysis: Definition and Algorithms
2006 Fortieth Asilomar Conference on Signals, Systems and Computers, 2006We present a new approach to independent component analysis (ICA) by extending the formulation of univariate source signals to multivariate source signals. The new approach is termed independent vector analysis (IVA). In the model, we assume that linear mixing model exists in each dimension separately, and the latent sources are independent of the ...
Taesu Kim, Intae Lee, Te-Won Lee
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Reweighted Algorithms for Independent Vector Analysis
IEEE Signal Processing Letters, 2017In this letter, we consider the problem of joint blind source separation of multiple datasets simultaneously using an Independent vector analysis (IVA) framework. In particular we propose a new paradigm of reweighted algorithms for IVA by employing a source prior from a multivariate generalized scale mixture distribution family.
Ritwik Giri +2 more
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Stable analysis of Fast Independent Vector Analysis algorithm
2014 IEEE International Conference on Communiction Problem-solving, 2014The Fast Independent Vector Analysis (FastIVA) algorithm [1] is an effective approach to achieve the joint blind source separation (JBSS) problem. However, the authors in [1] do not give the stable analysis for the FastIVA algorithm. In this paper, we extend the work of Intae Lee et al [1] by deriving conditions for local stability for the more general
Guobing Qian, Liping Li, Hongshu Liao
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Performance Bounds for Complex-Valued Independent Vector Analysis
IEEE Transactions on Signal Processing, 2020Independent Vector Analysis (IVA) is a method for joint Blind Source Separation of multiple datasets with wide area of applications including audio source separation, biomedical data analysis, etc. In this paper, identification conditions and Cramer-Rao Lower Bound (CRLB) on the achievable accuracy are derived for the complex-valued case involving ...
Vaclav Kautsky +3 more
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Subspace independent component analysis using vector kurtosis
Pattern Recognition, 2006This discussion presents a new perspective of subspace independent component analysis (ICA). The notion of a function of cumulants (kurtosis) is generalized to vector kurtosis. This vector kurtosis is utilized in the subspace ICA algorithm to estimate subspace independent components.
Sharma, Alok, Paliwal, Kuldip K
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Independent vector analysis for SSVEP signal enhancement
2015 49th Annual Conference on Information Sciences and Systems (CISS), 2015Steady state visual evoked potentials (SSVEP) have been identified as a highly viable solution for brain computer interface (BCI) systems. The SSVEP is observed in the scalp-based recordings of electroencephalogram (EEG) signals, and is one component buried amongst the normal brain signals and complex noise.
Darren K. Emge +3 more
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Auxiliary function approach to independent component analysis and independent vector analysis
SPIE Proceedings, 2015In this paper, we review an auxiliary function approach to independent component analysis (ICA) and independent vector analysis (IVA). The derived algorithm consists of two alternative updates: 1) weighted covariance matrix update and 2) demixing matrix update, which include no tuning parameters such as a step size in the gradient descent method.
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Independent component analysis of quaternion Gaussian vectors
2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, 2010This paper addresses the independent component analysis (ICA) of quaternion Gaussian vectors. Firstly, we define the properness profile of a quaternion random variable, which can be seen as the quaternion analogue of the circularity coefficients of complex vectors.
Javier Via +3 more
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Sparse Independent Vector Analysis Based on Mel Filter
2020 IEEE 9th Global Conference on Consumer Electronics (GCCE), 2020To make independent vector analysis (IVA) robust for whitening, sparse IVA clips spectrum in high frequency bands, because whitening generates artificial noise in high frequency bands. In this paper, to avoid clipping of source spectrum by sparse IVA, we propose an application of Mel filter to the observed spectrum before clipping in order to emphasize
Takahiro Ushijima +3 more
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