An Information Maximization Approach To Blind Separation And Blind Deconvolution Pdf

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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.

An Information-Maximization Approach to Blind Separation and Blind Deconvolution

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 practical goal of these methods is to produce a set of output signals which are much more intelligible and listenable than the mixture signals, without any prior information about the signals being separated, the room reverberation characteristics, or the room impulse response.

Unable to display preview. Download preview PDF. Skip to main content. This service is more advanced with JavaScript available. Advertisement Hide. This process is experimental and the keywords may be updated as the learning algorithm improves. This is a preview of subscription content, log in to check access. Communi-cations, vol. CrossRef Google Scholar. Communications, vol.

Benveniste, M. Goursat, and G. Automatic Control, vol. Shalvi and E. Theory, vol. Thorpe and O. Zangwill, Eds. Cambridge University Press, , pp. Google Scholar. Cover and J. Thomas, Elements of Information Theory. Wiley Series in Telecommunications, Nadal and N. Bell and T. Tong, Y.

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Blind Source Separation

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.

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.

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.

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1 Response
  1. Jay P.

    We derive a new self-organizing learning algorithm that maximizes the information transferred in a network of nonlinear units.

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