Results 251 to 260 of about 2,524,223 (282)

Supervised independent vector analysis through pilot dependent components

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
Unknown 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), 2015
An 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, Matthew Anderson, Tulay Adali   +2 more
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Independent vector analysis for real world speech processing

SPIE Proceedings, 2007
We introduce independent vector analysis (IVA) which is an extension of independent component analysis (ICA) to multivariate components. In a set of ICA mixtures, IVA groups dependent source components across different ICA mixtures and regard them as a multivariate source.
Intae Lee, Te-Won Lee
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Independent vector analysis incorporating active and inactive states

2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
Independent vector analysis (IVA) is a method for separating convolutedly mixed signals that avoids the well-known permutation problem in frequency domain blind source separation (BSS). In this paper, we exploit the nonstationarity of signals, a common feature, for BSS.
Alireza Masnadi-Shirazi, Bhaskar Rao
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Independent vector analysis with sparse inverse covariance estimation

2023
A fundamental task in the analysis of multiple sets of data is the source recovery problem when little is known about the observed data. Real-world applications for this problem include the analysis of medical imaging such as fMRIs, multi-modal disinformation detection, video surveillance, and molecular data fusion, among others.
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Combining Independent Component Analysis with Support Vector Machines

2006 1st International Symposium on Systems and Control in Aerospace and Astronautics, 2006
Recently, support vector machine (SVM) has become a popular tool in pattern recognition. In developing a successful SVM classifier, the first step is feature extraction. This paper proposes the application of independent component analysis (ICA) to SVM for feature extraction.
null Genting Yan, null Guangfu Ma, J. Lv, null Bin Song   +3 more
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Independent Vector Analysis: Theory, Algorithms, and Applications

2013
The field of blind source separation (BSS) is a well studied discipline within the signal processing community due to its applicability to a variety of problems when the data observation model is poorly known or difficult to model. For example, in the study of the human brain with functional magnetic resonance imaging (fMRI), a neuroimaging sensor, BSS
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Spatially-Regularized Switching Independent Vector Analysis

2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2023
Tetsuya Ueda, Tomohiro Nakatani, Rintaro Ikeshita, Shoko Araki, Shoji Makino   +4 more
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Nonorthogonal Independent Vector Analysis Using Multivariate Gaussian Model

2010
We consider the problem of joint blind source separation of multiple datasets and introduce an effective solution to the problem. We pose the problem in an independent vector analysis (IVA) framework utilizing the multivariate Gaussian source vector distribution.
Matthew Anderson, Xi-Lin Li, Tülay Adalı   +2 more
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Independent vector analysis: Model, applications, challenges

Pattern Recognition, 2023
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