An Information Maximization Approach To Blind Separation And Blind Deconvolution Pdf
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- An information-maximization approach to blind separation and blind deconvolution
- CNL Publication Search: Bell, A. J.
- Convolutive Blind Source Separation for Audio Signals
- An Information-Maximization Approach to Blind Separation and Blind Deconvolution
The present invention relates generally to methods and apparatus for separating signal sources and more specifically to the blind separation of multiple sources of radio signals. Radio frequency RF spectrum is a scarce resource. In the cellular or personal communications systems PCS environment an increasing number of users needs to be serviced at the same time avoiding simultaneous users interfering with each other.
An information-maximization approach to blind separation and blind deconvolution
In this paper, we propose a new blind multichannel adaptive filtering scheme, which incorporates a partial-updating mechanism in the error gradient of the update equation. The proposed blind processing algorithm operates in the time-domain by updating only a selected portion of the adaptive filters. The algorithm steers all computational resources to filter taps having the largest magnitude gradient components on the error surface. Therefore, it requires only a small number of updates at each iteration and can substantially minimize overall computational complexity. Numerical experiments carried out in realistic blind identification scenarios indicate that the performance of the proposed algorithm is comparable to the performance of its full-update counterpart, but with the added benefit of a highly reduced computational complexity. In recent years, blind source separation BSS has received much attention due to its strong potential for use in a variety of applications, such as automatic speech recognition, hearing aid devices and hands-free telephony.
CNL Publication Search: Bell, A. J.
All rights reserved. Neural Computation has become the leading journal of its kind. The editors of the book are Geoffrey Hinton and Terrence Sejnowski, two pioneers in neural networks. The edited volume provides a sample of important works on unsupervised learning, which cut across the fields of. Downloads PDF.
Blind Speech Separation pp Cite as. In this era of ever-improving communications technologies, we have become used to conversing with others across the globe. Invariably, a real-time telephone conversation begins with a microphone or other audio recording device. Noise in the environment can corrupt our speech signal as it is being recorded, making it harder to both use and understand further down the communications pathway. Other talkers in the environment add their own auditory interference to the conversation. Recent work in advanced signal processing has resulted in new and promising technologies for recovering speech signals that have been corrupted by speech-like and other types of interference. Termed blind source separation methods, or BSS methods for short, these techniques rely on the diversity provided by the collection of multichannel data by an array of distant microphones sensors in room environments.
The present invention relates generally to methods and apparatus for separating signal sources and more specifically to the blind separation of multiple sources of radio signals. Radio frequency RF spectrum is a scarce resource. In the cellular or personal communications systems PCS environment an increasing number of users needs to be serviced at the same time avoiding simultaneous users interfering with each other. One way to pack multiple simultaneous users on the same frequency band is spatial division multiple access SDMA. The purpose of SDMA is to separate the radio signals of interfering users either intentional or accidental from each others on the basis of the differing spatial characteristics of different user signals.
Convolutive Blind Source Separation for Audio Signals
Most of these publications can be downloaded in PDF format. They are made available for individual use only; contact the publisher and the author to receive permission for reproduction in any form. Sejnowski starting in
Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. An Information-Maximization Approach to Blind Separation and Blind Deconvolution Abstract: We derive a new self-organizing learning algorithm that maximizes the information transferred in a network of nonlinear units. The algorithm does not assume any knowledge of the input distributions, and is defined here for the zero-noise limit.
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An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Infomax is an optimization principle for artificial neural networks and other information processing systems. Infomax algorithms are learning algorithms that perform this optimization process. The principle was described by Linsker in Infomax, in its zero-noise limit, is related to the principle of redundancy reduction proposed for biological sensory processing by Horace Barlow in ,  and applied quantitatively to retinal processing by Atick and Redlich. One of the applications of infomax has been to an independent component analysis algorithm that finds independent signals by maximizing entropy.
Metrics details. We derive new fixed-point algorithms for the blind separation of complex-valued mixtures of independent, noncircularly symmetric, and non-Gaussian source signals. Leveraging recently developed results on the separability of complex-valued signal mixtures, we systematically construct iterative procedures on a kurtosis-based contrast whose evolutionary characteristics are identical to those of the FastICA algorithm of Hyvarinen and Oja in the real-valued mixture case. Thus, our methods inherit the fast convergence properties, computational simplicity, and ease of use of the FastICA algorithm while at the same time extending this class of techniques to complex signal mixtures. For extracting multiple sources, symmetric and asymmetric signal deflation procedures can be employed. Simulations for both noiseless and noisy mixtures indicate that the proposed algorithms have superior finite-sample performance in data-starved scenarios as compared to existing complex ICA methods while performing about as well as the best of these techniques for larger data-record lengths. Electronics Letters , 30 17
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