Topic
Gaussian process
About: Gaussian process is a research topic. Over the lifetime, 18944 publications have been published within this topic receiving 486645 citations. The topic is also known as: Gaussian stochastic process.
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TL;DR: This paper proposes a class of VAD algorithms based on several statistical models based on the Gaussian model, and incorporates the complex Laplacian and Gamma probability density functions to the analysis of statistical properties.
Abstract: One of the key issues in practical speech processing is to achieve robust voice activity detection (VAD) against the background noise. Most of the statistical model-based approaches have tried to employ the Gaussian assumption in the discrete Fourier transform (DFT) domain, which, however, deviates from the real observation. In this paper, we propose a class of VAD algorithms based on several statistical models. In addition to the Gaussian model, we also incorporate the complex Laplacian and Gamma probability density functions to our analysis of statistical properties. With a goodness-of-fit tests, we analyze the statistical properties of the DFT spectra of the noisy speech under various noise conditions. Based on the statistical analysis, the likelihood ratio test under the given statistical models is established for the purpose of VAD. Since the statistical characteristics of the speech signal are differently affected by the noise types and levels, to cope with the time-varying environments, our approach is aimed at finding adaptively an appropriate statistical model in an online fashion. The performance of the proposed VAD approaches in both the stationary and nonstationary noise environments is evaluated with the aid of an objective measure.
241 citations
01 Jan 2003
TL;DR: Nonlinear Classification, Approximation Theory and Signal Processing, Modeling of Complex Objects, and Splines Gaussian Processes and Support Vector Machines.
Abstract: Nonlinear Classification * Approximation Theory and Signal Processing * Modeling of Complex Objects * Splines Gaussian Processes and Support Vector Machines * Case Studies * Theory * Machine Learning and Optimization * Future Directions
241 citations
TL;DR: An asymptotic equipartition theorem for nonstationary Gaussian processes is proved and it is proved that the feedback capacity C/sub FB/ in bits per transmission and the nonfeedback capacity C satisfy C > C >.
Abstract: The capacity of time-varying additive Gaussian noise channels with feedback is characterized. Toward this end, an asymptotic equipartition theorem for nonstationary Gaussian processes is proved. Then, with the aid of certain matrix inequalities, it is proved that the feedback capacity C/sub FB/ in bits per transmission and the nonfeedback capacity C satisfy C >
240 citations
TL;DR: A Bayesian procedure based on Gaussian process models using a nonstationary covariance function is proposed and experiments proved the approach effectiveness with an excellent prediction and a good tracking.
239 citations