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Michael Beer - One of the best experts on this subject based on the ideXlab platform.

  • the Bhattacharyya Distance enriching the p box in stochastic sensitivity analysis
    Mechanical Systems and Signal Processing, 2019
    Co-Authors: Matteo Broggi, Michael Beer, Pengfei Wei
    Abstract:

    Abstract The tendency of uncertainty analysis has promoted the transformation of sensitivity analysis from the deterministic sense to the stochastic sense. This work proposes a stochastic sensitivity analysis framework using the Bhattacharyya Distance as a novel uncertainty quantification metric. The Bhattacharyya Distance is utilised to provide a quantitative description of the P-box in a two-level procedure for both aleatory and epistemic uncertainties. In the first level, the aleatory uncertainty is quantified by a Monte Carlo process within the probability space of the cumulative distribution function. For each sample of the Monte Carlo simulation, the second level is performed to propagate the epistemic uncertainty by solving an optimisation problem. Subsequently, three sensitivity indices are defined based on the Bhattacharyya Distance, making it possible to rank the significance of the parameters according to the reduction and dispersion of the uncertainty space of the system outputs. A tutorial case study is provided in the first part of the example to give a clear understanding of the principle of the approach with reproducible results. The second case study is the NASA Langley challenge problem, which demonstrates the feasibility of the proposed approach, as well as the Bhattacharyya Distance metric, in solving such a large-scale, strong-nonlinear, and complex problem.

  • the role of the Bhattacharyya Distance in stochastic model updating
    Mechanical Systems and Signal Processing, 2019
    Co-Authors: Matteo Broggi, Michael Beer
    Abstract:

    Abstract The Bhattacharyya Distance is a stochastic measurement between two samples and taking into account their probability distributions. The objective of this work is to further generalize the application of the Bhattacharyya Distance as a novel uncertainty quantification metric by developing an approximate Bayesian computation model updating framework, in which the Bhattacharyya Distance is fully embedded. The Bhattacharyya Distance between sample sets is evaluated via a binning algorithm. And then the approximate likelihood function built upon the concept of the Distance is developed in a two-step Bayesian updating framework, where the Euclidian and Bhattacharyya Distances are utilized in the first and second steps, respectively. The performance of the proposed procedure is demonstrated with two exemplary applications, a simulated mass-spring example and a quite challenging benchmark problem for uncertainty treatment. These examples demonstrate a gain in quality of the stochastic updating by utilizing the superior features of the Bhattacharyya Distance, representing a convenient, efficient, and capable metric for stochastic model updating and uncertainty characterization.

Branko Kovacevic - One of the best experts on this subject based on the ideXlab platform.

Kong Aik Lee - One of the best experts on this subject based on the ideXlab platform.

  • a gmm supervector kernel with the Bhattacharyya Distance for svm based speaker recognition
    International Conference on Acoustics Speech and Signal Processing, 2009
    Co-Authors: Chang Huai You, Kong Aik Lee
    Abstract:

    Gaussian mixture model (GMM) supervector is one of the effective techniques in text independent speaker recognition. In our previous work, we introduce the GMM-UBM mean interval (GUMI) concept based on the Bhattacharyya Distance. Subsequently GUMI kernel was successfully used in conjunction with support vector machine (SVM) for speaker recognition. Besides the first order statistics, it is generally believed that speaker cues are also partly conveyed by second order statistics. In this paper, we extend the Bhattacharyya-based SVM kernel by constructing the supervector with the mean statistical vector and the covariance statistical vector. Comparing with the Kullback-Leibler (KL) kernel, we demonstrate the effectiveness of the new kernel on the 2006 National Institute of Standards and Technology (NIST) speaker recognition evaluation (SRE) dataset.

  • an svm kernel with gmm supervector based on the Bhattacharyya Distance for speaker recognition
    IEEE Signal Processing Letters, 2009
    Co-Authors: Chang Huai You, Kong Aik Lee
    Abstract:

    Gaussian mixture model (GMM) and support vector machine (SVM) have become popular classifiers in text-independent speaker recognition. A GMM-supervector characterizes a speaker's voice with the parameters of GMM, which include mean vectors, covariance matrices, and mixture weights. GMM-supervector SVM benefits from both GMM and SVM frameworks to achieve the state-of-the-art performance. Conventional Kullback-Leibler (KL) kernel in GMM-supervector SVM classifier limits the adaptation of GMM to mean value and leaves covariance unchanged. In this letter, we introduce the GMM-UBM mean interval (GUMI) concept based on the Bhattacharyya Distance. This leads to a new kernel for SVM classifier. Comparing with the KL kernel, the new kernel allows us to exploit the information not only from the mean but also from the covariance. We demonstrate the effectiveness of the new kernel on the 2006 National Institute of Standards and Technology (NIST) speaker recognition evaluation (SRE) dataset.

Milan Markovic - One of the best experts on this subject based on the ideXlab platform.

Milan Milosavljevic - One of the best experts on this subject based on the ideXlab platform.