The Experts below are selected from a list of 45015 Experts worldwide ranked by ideXlab platform

D.g. Bhalke - One of the best experts on this subject based on the ideXlab platform.

  • Speaker verification using Gaussian Mixture Model
    2015 International Conference on Pervasive Computing (ICPC), 2015
    Co-Authors: Shilpa S. Jagtap, D.g. Bhalke
    Abstract:

    In this paper, speaker verification system using Gaussian Mixture Model (GMM) is proposed. The proposed system consists of pre-processing, feature extraction, Modelling and classification stage. The pre-processing is used to remove silent part of signal to reduce computational complexity. Pitch frequency and Mel Frequency Cepstral Coefficients(MFCC)are used as a feature vector for speaker verification system. Modelling is done using different combination of Gaussian Mixture Models. Simple distance measures are used for the classification between reference and the test signal.

Rabih A. Jabr - One of the best experts on this subject based on the ideXlab platform.

  • Statistical Representation of Distribution System Loads Using Gaussian Mixture Model
    IEEE Transactions on Power Systems, 2010
    Co-Authors: R. Singh, B C Pal, Rabih A. Jabr
    Abstract:

    This paper presents a probabilistic approach for statistical Modeling of the loads in distribution networks. In a distribution network, the probability density functions (pdfs) of loads at different buses show a number of variations and cannot be represented by any specific distribution. The approach presented in this paper represents all the load pdfs through Gaussian Mixture Model (GMM). The expectation maximization (EM) algorithm is used to obtain the parameters of the Mixture components. The performance of the method is demonstrated on a 95-bus generic distribution network Model.

Shilpa S. Jagtap - One of the best experts on this subject based on the ideXlab platform.

  • Speaker verification using Gaussian Mixture Model
    2015 International Conference on Pervasive Computing (ICPC), 2015
    Co-Authors: Shilpa S. Jagtap, D.g. Bhalke
    Abstract:

    In this paper, speaker verification system using Gaussian Mixture Model (GMM) is proposed. The proposed system consists of pre-processing, feature extraction, Modelling and classification stage. The pre-processing is used to remove silent part of signal to reduce computational complexity. Pitch frequency and Mel Frequency Cepstral Coefficients(MFCC)are used as a feature vector for speaker verification system. Modelling is done using different combination of Gaussian Mixture Models. Simple distance measures are used for the classification between reference and the test signal.

Ai Hui Tan - One of the best experts on this subject based on the ideXlab platform.

  • from a Gaussian Mixture Model to nonadditive fuzzy systems
    IEEE Transactions on Fuzzy Systems, 2005
    Co-Authors: Mingtao Gan, Madasu Hanmandlu, Ai Hui Tan
    Abstract:

    This work explores how a kind of probabilistic system, namely the Gaussian Mixture Model (GMM), can be translated to an additive fuzzy system. We will prove the mathematical equivalence between the conditional mean of a GMM, and the defuzzified output of a generalized fuzzy Model (GFM). The relationship between a GMM and a GFM, and the conditions for GMM to GFM translation will be made explicit in the form of theorems. The work will then extend to special cases of the GFM, specifically the Mamdani-Larsen and Takagi-Sugeno fuzzy Models. The possibility of reverse translation, that is, from a GFM to a GMM will also be discussed. Finally, we will consider the generality of a GMM, specifically how it can approximate other distribution functions.

R. Singh - One of the best experts on this subject based on the ideXlab platform.

  • Statistical Representation of Distribution System Loads Using Gaussian Mixture Model
    IEEE Transactions on Power Systems, 2010
    Co-Authors: R. Singh, B C Pal, Rabih A. Jabr
    Abstract:

    This paper presents a probabilistic approach for statistical Modeling of the loads in distribution networks. In a distribution network, the probability density functions (pdfs) of loads at different buses show a number of variations and cannot be represented by any specific distribution. The approach presented in this paper represents all the load pdfs through Gaussian Mixture Model (GMM). The expectation maximization (EM) algorithm is used to obtain the parameters of the Mixture components. The performance of the method is demonstrated on a 95-bus generic distribution network Model.