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Independent Vector Analysis Applied to Remove Muscle Artifacts in EEG Data
IEEE Transactions on Instrumentation and Measurement, 2017Xun Chen +3 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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Geometrically Constrained Independent Vector Analysis for Directional Speech Enhancement
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020This paper addresses the multichannel directional speech enhancement problem with geometrically constrained independent vector analysis (GCIVA), where we aim to combine the high separation performance from blind source separation and the capability of ...
Li Li, K. Koishida
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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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Online Directional Speech Enhancement Using Geometrically Constrained Independent Vector Analysis
Interspeech, 2020This paper proposes an online dual-microphone system for directional speech enhancement, which employs geometrically constrained independent vector analysis (IVA) based on the auxiliary function approach and vectorwise coordinate descent.
Li Li, K. Koishida, S. Makino
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Supervised independent vector analysis through pilot dependent components
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017Unknown global permutation of the separated sources, time-varying source activity and under determination are common problems affecting on-line Independent Vector Analysis when applied to real-world speech enhancement. In this work we propose to extend the signal model of IVA by introducing additional supervising components.
Francesco Nesta, Zbynek Koldovsky
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Independent vector analysis by entropy rate bound minimization
2015 49th Annual Conference on Information Sciences and Systems (CISS), 2015An extension of independent component analysis from one to multiple datasets, independent vector analysis, has recently become a subject of significant research interest. Since in many applications, latent sources are non-Gaussian, have sample dependence, and have dependence across multiple data sets, it is desirable to exploit all these properties ...
Geng-Shen Fu +2 more
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