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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: The experimental results show that computational reduction by a factor of 17 can be achieved with 5% relative reduction in equal error rate (EER) compared with the baseline, and the SGMM-SBM shows some advantages over the recently proposed hash GMM, including higher speed and better verification performance.
Abstract: We present an integrated system with structural Gaussian mixture models (SGMMs) and a neural network for purposes of achieving both computational efficiency and high accuracy in text-independent speaker verification. A structural background model (SBM) is constructed first by hierarchically clustering all Gaussian mixture components in a universal background model (UBM). In this way the acoustic space is partitioned into multiple regions in different levels of resolution. For each target speaker, a SGMM can be generated through multilevel maximum a posteriori (MAP) adaptation from the SBM. During test, only a small subset of Gaussian mixture components are scored for each feature vector in order to reduce the computational cost significantly. Furthermore, the scores obtained in different layers of the tree-structured models are combined via a neural network for final decision. Different configurations are compared in the experiments conducted on the telephony speech data used in the NIST speaker verification evaluation. The experimental results show that computational reduction by a factor of 17 can be achieved with 5% relative reduction in equal error rate (EER) compared with the baseline. The SGMM-SBM also shows some advantages over the recently proposed hash GMM, including higher speed and better verification performance.

117 citations

Journal ArticleDOI
TL;DR: In this paper, the authors consider the empirical process indexed by a set of functions analytic on an open domain of the complex plane including the support of the semicircle law and prove that this empirical process converges to a Gaussian process.
Abstract: It is well known that the spectral distribution Fn of a Wigner matrix converges to Wigner's semicircle law. We consider the empirical process indexed by a set of functions analytic on an open domain of the complex plane including the support of the semicircle law. Under fourth-moment conditions, we prove that this empirical process converges to a Gaussian process. Explicit formulae for the mean function and the covariance function of the limit process are provided.

117 citations

Journal ArticleDOI
TL;DR: The spectral techniques of transfer function estimation of linear systems to time invariant quadratic systems when a stationary Gaussian process is used as a driving function is extended.
Abstract: In recent years the use of stationary random inputs as a forcing function to experimentally determine the transfer function of linear systems has become widespread. The procedure involves the measurement of spectra and crossspectra between the input and output and the formation of the proper ratio. There are two basic reasons for using random testing functions. For many situations, particularly mechanical ones, these inputs are easier to generate than say steps or impulses and frequently they can be made to more closely approximate the in-service input. (The latter attribute is an advantage since the linearity of the system may not be complete but may be sufficiently so over the operating range of interest). A simple measure of linearity is immediately available i.e., the coherency. Although many experimenters try to generate a Gaussian stationary process for the forcing function this is not necessary from an expected value veiwpoint. Actually the probability structure plays no role in the general logic of the detailed procedure since only second moment characteristics of the process are relevant to the expected value calculations. However, a Gaussian input can be convenient since sometimes the evaluation of the variability of the estimates is simplified. In studying higher order systems by driving them with a random forcing function this use of a Gaussian process makes the calculations of the expected values considerably easier. In this paper we shall extend the spectral techniques of transfer function estimation of linear systems to time invariant quadratic systems when a stationary Gaussian process is used as a driving function.

116 citations

Journal ArticleDOI
TL;DR: Maximum likelihood (ML) methods for jointly estimating the target and clutter parameters in compound-Gaussian clutter using radar array measurements are developed and Parameter-expanded expectation-maximization (PX-EM) algorithms are developed to compute the ML estimates of the unknown parameters.
Abstract: Compound-Gaussian models are used in radar signal processing to describe heavy-tailed clutter distributions. The important problems in compound-Gaussian clutter modeling are choosing the texture distribution, and estimating its parameters. Many texture distributions have been studied, and their parameters are typically estimated using statistically suboptimal approaches. We develop maximum likelihood (ML) methods for jointly estimating the target and clutter parameters in compound-Gaussian clutter using radar array measurements. In particular, we estimate i) the complex target amplitudes, ii) a spatial and temporal covariance matrix of the speckle component, and iii) texture distribution parameters. Parameter-expanded expectation-maximization (PX-EM) algorithms are developed to compute the ML estimates of the unknown parameters. We also derived the Cramer-Rao bounds (CRBs) and related bounds for these parameters. We first derive general CRB expressions under an arbitrary texture model then simplify them for specific texture distributions. We consider the widely used gamma texture model, and propose an inverse-gamma texture model, leading to a complex multivariate t clutter distribution and closed-form expressions of the CRB. We study the performance of the proposed methods via numerical simulations

116 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023502
20221,181
20211,132
20201,220
20191,119
2018978