Results 211 to 220 of about 28,063 (251)

Blind source separation

2019
This paper presents a mathematical approach to methods of blind sources separation(BSS) such as principal component analysis(PCA) and independent component analysis(ICA).
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Blind source separation: a unified approach

Neurocomputing, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Singh, Yogesh, Rai, C. S.
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Nonlinear blind source separation using kernels

IEEE Transactions on Neural Networks, 2003
We derive a new method for solving nonlinear blind source separation (BSS) problems by exploiting second-order statistics in a kernel induced feature space. This paper extends a new and efficient closed-form linear algorithm to the nonlinear domain using the kernel trick originally applied in support vector machines (SVMs).
Martinez, Dominique, Bray, Alistair
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Kernel-Based Nonlinear Blind Source Separation

Neural Computation, 2003
We propose kTDSEP, a kernel-based algorithm for nonlinear blind source separation (BSS). It combines complementary research fields: kernel feature spaces and BSS using temporal information. This yields an efficient algorithm for nonlinear BSS with invertible nonlinearity.
Harmeling, S., Ziehe, A., Kawanabe, M., Müller, K.   +3 more
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Blind Source Separation of Peripheral Nerve Recordings

Journal of Neural Engineering, 2007
Prosthetic devices can be controlled using signals recorded in parts of the body where sensation and/or voluntary movement have been retained. Although neural prosthetic applications have used single-channel recordings, multiple-channel recordings could provide a significant increase in useable control signals.
W, Tesfayesus, D M, Durand
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Noise source separation based on the blind source separation

2011 Chinese Control and Decision Conference (CCDC), 2011
The identification of acoustic source accurately is a fundamental problem in noise control. In the practical project, if the contribution of multi-source-noise to the whole was identified, and then the noise level can be reduced accordingly. To get the accurate noise signal, measurements should be possible while the machines are constantly in action ...
Yang Yang, Zuoli Li, Xiuqin Wang, Di Zhang   +3 more
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Robust Blind Source Separation by Beta Divergence

Neural Computation, 2002
Blind source separation is aimed at recovering original independent signals when their linear mixtures are observed. Various methods for estimating a recovering matrix have been proposed and applied to data in many fields, such as biological signal processing, communication engineering, and financial market data analysis.
Mihoko, Minami, Eguchi, Shinto
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Proportionate Algorithms for Blind Source Separation

2014
In this paper we propose an extension of time-domain Blind Source Separation algorithms by applying the well known proportionate and improved proportionate adaptive algorithms. These algorithms, known in the context of adaptive filtering, are able to use the sparseness of acoustic impulse responses of mixing environments and give better performances ...
SCARPINITI, MICHELE, COMMINIELLO, DANILO, SCARDAPANE, SIMONE, PARISI, Raffaele, UNCINI, Aurelio   +4 more
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Only Mostly Blind Source Separation

2011 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2011
In acoustic and bioelectrical environments characterized by multiple simultaneous sources, effective blind source separation from sensor response mixtures becomes difficult as the number of sources increases—especially when the true number of sources is both unknown and changing over time.
Richard Goldhor, Keith Gilbert, Joel MacAuslan, Karen Payton   +3 more
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Blind source separation with noisy sources

Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics, 2002
A new method of source separation with noisy observations is proposed in the case of two sensors. Each observation contains a mixture of two signals with noise. The objective is to estimate the frequency spectra of the linear filters that combine the two signals in the data stream.
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