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Title:

Über Verbrechen aus indirekter Absicht

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1791

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Title:

Etwas über den Ursprung, Begriff, Geschichte des Eydes, und die Moralität der jezt gewöhnlichen Eydesformeln

Publisher:

Ludwig-Maximilians-Universität München

Year of Publication:

1790-01-01

Document Type:

Digitalisat ; NonPeerReviewed

Language:

deu

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ddc:090

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http://epub.ub.uni-muenchen.de/11111/1/8Jus2864_1.pdf ; Hoff, August von: Etwas über den Ursprung, Begriff, Geschichte des Eydes, und die Moralität der jezt gewöhnlichen Eydesformeln. Berlin: Kön. Preuß. Akad. Kunst- und Buchh., 1790

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Title:

Two-Stage Approach for Multichannel Blind Deconvolution

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Multichannel blind deconvolution is a method that allows the separation of sources from which only convolutive mixtures are observable. It is an attractive technique for separating independent speech and noise signals. Fast tracking is essential in order to cope with real nonstationary environments. Based on the principle of information maximiza...

Multichannel blind deconvolution is a method that allows the separation of sources from which only convolutive mixtures are observable. It is an attractive technique for separating independent speech and noise signals. Fast tracking is essential in order to cope with real nonstationary environments. Based on the principle of information maximization, a new two-stage algorithm is proposed which uses a rational demixing matrix. Compared to the usual polynomial approximation, the number of parameters to be adapted is drastically reduced, resulting in a significant increase of the convergence speed. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-19

Source:

http://www.isi.ee.ethz.ch/archive/publications/isipap/tpvh-anka-99-iwaenc.pdf

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Document Type:

text

Language:

en

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Title:

Step-Size Control in Blind Source Separation

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The behavior of the classic algorithm for blind source separation (BSS) is detailed for a fixed step size. To improve the algorithm in speed and exactness, essential in tracking a time-varying mixing environment, a variable step size must be employed. The ideal step size should decrease or increase as the overall system error decreases or increa...

The behavior of the classic algorithm for blind source separation (BSS) is detailed for a fixed step size. To improve the algorithm in speed and exactness, essential in tracking a time-varying mixing environment, a variable step size must be employed. The ideal step size should decrease or increase as the overall system error decreases or increases. It is shown analytically that the coefficients of the estimating function provide a "measure of error" that is available to automatically control the algorithm step size. This paper proposes a self-adjusting, time-varying step size that is built from the square of the running average of the coefficients of the estimating function. Error free convergence is achieved for a time-invariant environment. The ability of the algorithm to improve the convergence in a time-invariant mixing environment and to track a changing mixing environment is demonstrated by extensive simulation results. 1. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-19

Source:

http://www.isi.ee.ethz.ch/archive/publications/isipap/tpvh-anka-00-ica.pdf

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Document Type:

text

Language:

en

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Title:

Transpose Properties in the Stability and Performance of the Classic Adaptive Algorithms for Blind Source Separation and Deconvolution

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This paper presents a tutorial review of the problem of Blind Source Separation (BSS) and the properties of the classic adaptive algorithms when either the score function or a general (non-score) nonlinearity is employed in the algorithm. In new findings it is shown that the separating solution for both sub- and super-Gaussian signals can be sta...

This paper presents a tutorial review of the problem of Blind Source Separation (BSS) and the properties of the classic adaptive algorithms when either the score function or a general (non-score) nonlinearity is employed in the algorithm. In new findings it is shown that the separating solution for both sub- and super-Gaussian signals can be stabilized by an algorithm employing any given nonlinearity. For these separating solutions the steady-state error levels are also given in terms of the nonlinearity and the pdf.s of the source signals. These results show that a transpose symmetry exists between the nonlinear algorithms for sub-and super-Gaussian signals. The behavior of the algorithm is then detailed when the ideal score-function nonlinearity is replaced by a general (hard saturation or u³) nonlinearity. The phases of convergence to decorrelated output signals and then to recovery of the source signals are explained. The results are then extended to single- and multi-channel. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2011-10-20

Source:

http://www.isi.ee.ethz.ch/publications/isipap/tpvh-anka-00-sp.ps

http://www.isi.ee.ethz.ch/publications/isipap/tpvh-anka-00-sp.ps Minimize

Document Type:

text

Language:

en

DDC:

518 Numerical analysis *(computed)*

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Title:

Two-Stage Approach For Multichannel Blind Deconvolution

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Multichannel blind deconvolution is a method that allows the separation of sources from which only convolutive mixtures are observable. It is an attractive technique for separating independent speech and noise signals. Fast tracking is essential in order to cope with real nonstationary environments. Based on the principle of information maximiza...

Multichannel blind deconvolution is a method that allows the separation of sources from which only convolutive mixtures are observable. It is an attractive technique for separating independent speech and noise signals. Fast tracking is essential in order to cope with real nonstationary environments. Based on the principle of information maximization, a new two-stage algorithm is proposed which uses a rational demixing matrix. Compared to the usual polynomial approximation, the number of parameters to be adapted is drastically reduced, resulting in a significant increase of the convergence speed. 1. INTRODUCTION Multichannel blind deconvolution is an attractive technique to isolate several independent speech signals (cocktail party situation) or to separate speech from disturbing sources. Generally, multichannel blind deconvolution is the task to recover independent unknown source signals, represented by the vector s, from an equal number of observed signals, represented by the vecto. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-14

Source:

http://www.isi.ee.ethz.ch/publications/isipap/tpvh-anka-99-iwaenc.ps

http://www.isi.ee.ethz.ch/publications/isipap/tpvh-anka-99-iwaenc.ps Minimize

Document Type:

text

Language:

en

DDC:

518 Numerical analysis *(computed)*

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Title:

Stability And Performance Of Adaptive Algorithms For Multichannel Blind Separation And Deconvolution

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The problem of blind source separation is reviewed and the properties of stability and performance of the classic adap# tive algorithms are derived. In case of an unstable separat# ing solution, a stabilization procedure is proposed. These #ndings are then extended to the problems of single-channel and multichannel blind deconvolution. It is sho...

The problem of blind source separation is reviewed and the properties of stability and performance of the classic adap# tive algorithms are derived. In case of an unstable separat# ing solution, a stabilization procedure is proposed. These #ndings are then extended to the problems of single-channel and multichannel blind deconvolution. It is shown that the algorithms for all these problems can be stabilized for any situation of nonlinearities and source distributions. 1 INTRODUCTION In the Multi-Channel Blind Deconvolution #MCBD# prob# lem we observe a signal vector x = #x1;x 2;x 3;::: ;x N # T that is formed from zero-mean source signals s = #s1 ;s 2;s 3;:::;s N # T by mixing and convolution #9#. Mathe# matically,inthez-domain this is represented by x#z#=A#z#s#z# ; #1# where A#z# is an invertible N # N matrix of polynomi# als. The goal is to recover the sources through the demix# ing#deconvolution process u#z#=W#z#x#z#=W#z#A#z#s#z#: #2# The N # N matrix W#z# is such that. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-15

Source:

http://www.isi.ee.ethz.ch/publications/isipap/tpvh-anka-00-eusipco.pdf

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Document Type:

text

Language:

en

DDC:

518 Numerical analysis *(computed)*

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Title:

Step-Size Control In Blind Source Separation

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Description:

The behavior of the classic algorithm for blind source separation (BSS) is detailed for a fixed step size. To improve the algorithm in speed and exactness, essential in tracking a time-varying mixing environment, a variable step size must be employed. The ideal step size should decrease or increase as the overall system error decreases or increa...

The behavior of the classic algorithm for blind source separation (BSS) is detailed for a fixed step size. To improve the algorithm in speed and exactness, essential in tracking a time-varying mixing environment, a variable step size must be employed. The ideal step size should decrease or increase as the overall system error decreases or increases. It is shown analytically that the coefficients of the estimating function provide a "measure of error" that is available to automatically control the algorithm step size. This paper proposes a self-adjusting, time-varying step size that is built from the square of the running average of the coefficients of the estimating function. Error free convergence is achieved for a time-invariant environment. The ability of the algorithm to improve the convergence in a time-invariant mixing environment and to track a changing mixing environment is demonstrated by extensive simulation results. 1. INTRODUCTION Blind Source Separation (BSS) is the prob. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-14

Source:

http://www.isi.ee.ethz.ch/publications/isipap/tpvh-anka-00-ica.ps

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Document Type:

text

Language:

en

DDC:

518 Numerical analysis *(computed)*

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Title:

Using Preprocessing in Blind Source Separation of Convolutive Mixtures to accelerate Convergence

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Blind source separation is an important task for applications in biomedical engineering, signal pro cessing and communications. In order to get to wards on-line time-variant applications, the conver gence speed of a separation algorithm has to be fast enough to track the changes of the environment. In this paper, a two-stage algorithm for blind ...

Blind source separation is an important task for applications in biomedical engineering, signal pro cessing and communications. In order to get to wards on-line time-variant applications, the conver gence speed of a separation algorithm has to be fast enough to track the changes of the environment. In this paper, a two-stage algorithm for blind source separation of convolutive mixtures in case of mini mum-phase mixing systems is presented. Thereby, a preprocessing of the observed signals is proposed in order to reduce the number of coeOEcients in the demixing system which is realized according to the information maximization approach by Bell and Se jnowski. The reduction of the amount of parame ters to adapt increases the algorithm's convergence speed signicantly. 1 Introduction Generally, blind signal separation is the task to re cover an unknown source signal vector s from a vector of observed mixed signals x, where the mix ing process is not known as well. At early stages, algorit. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-14

Source:

http://www.isi.ee.ethz.ch/publications/isipap/tpvh-anka-99-ecctd.ps

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Document Type:

text

Language:

en

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