M.K. Sonmez

University of Maryland, Baltimore, Baltimore, Maryland, United States

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Publications (8)10.89 Total impact

  • Source
    D.K. Emge, T. Adali, M. Kemal Sonmez
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    ABSTRACT: The aim of the work is to develop a flexible and efficient approach to the classification of the ratio of voiced to unvoiced excitation sources in continuous speech. To achieve this aim we adopt a probabilistic neural network approach. This is accomplished by designing a multilayer perceptron classifier trained by steepest descent minimization of the least relative entropy (LRE) cost function. By using the LRE cost function we can directly output the ratio, as a probability, of excitation source, voiced to unvoiced, for a given speech segment. These output probabilities can then be used directly in other applications, such as low bit rate coders
    Neural Networks, 1999. IJCNN '99. International Joint Conference on; 02/1999
  • Source
    Tulay Adali, Bora Bakal, M. Kemal Sonmez, Reza Fakory
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    ABSTRACT: This paper presents a time delay neural network (TDNN) model designed for the prediction of nitrogen oxides (NOx ) and carbon monoxide (CO) emissions from a fossil fuel power plant. NOx and CO emissions of the plant are determined as a function of other related time-series such as air flow rates and oxygen levels that are measured during the system operation. Correlation analysis is performed on the data to determine the location and the spread of cross-correlation between pairs of variables and this information is used to form a variable tapped delay line at the input of the network. We also introduce a neural network based preprocessor which employs an iterative regularization scheme to recover missing portions of CO data that are censored due to saturation of the measuring device. Prediction after training with the restored data set is observed to be significantly more accurate. Keywords: Time delay neural networks, environmental application, missing data prediction, NOx and CO pred...
    08/1998;
  • Neurocomputing 01/1997; 15(3). · 1.63 Impact Factor
  • Source
    T. Adali, M.K. Sonmez, Xiao Liu
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    ABSTRACT: We introduce a unified statistical framework for real-time signal processing with neural networks by using a recent extension of maximum likelihood (ML) estimation, partial likelihood (PL) estimation theory, which allows for (i) dependent observations, and (ii) processing of data using only the information that is available at the time of processing. For a general neural network conditional distribution model, we establish a fundamental information-theoretic relationship for PL estimation, and obtain large sample properties of PL for the general case of dependent observations. We consider applications of PL to prediction and channel equalization
    Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on; 06/1996
  • Tulay Adali, M. Kemal Sonmez
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    ABSTRACT: We formulate the adaptive channel equalization as a conditional probability distribution learning problem. Conditional probability density function of the transmitted signal given the received signal is parametrized by a sigmoidal perceptron. In this framework, we use relative entropy (Kullback -Leibler distance) between the true and the estimated distributions as the cost function to be minimized. The true probabilities are approximated by their stochastic estimators resulting in a stochastic relative entropy cost function. This function is well-formed in the sense of Wittner and Denker, therefore gradient descent on this cost function is guaranteed to find a solution. The consistency and asymptotic normality of this learning scheme are shown via Maximum Partial Likelihood estimation of logistic models. As a practical example, we demonstrate that the resulting algorithm successfully equalizes multipath channels. 1. INTRODUCTION Adaptive equalization techniques developed during the la...
    06/1995;
  • Source
    T. Adali, M.K. Sonmez, K. Patel
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    ABSTRACT: We present the general formulation for the adaptive equalization by distribution learning introduced by Adali (see Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, vol.3, p.297-300, April 1994) In this framework, adaptive equalization can be viewed as a parametrized conditional distribution estimation problem where the parameter estimation is achieved by learning on a multilayer perceptron (MLP). Depending on the definition of the conditioning event set either supervised or unsupervised (blind) algorithms in either recurrent or feedforward networks result. We derive the least relative entropy (LRE) algorithm for binary data communications and analyze its statistical and dynamical properties. Particularly, we show that LRE learning is consistent and asymptotically normal by working in the partial likelihood estimation framework, and that the algorithm can always recover from convergence at the wrong extreme as opposed to the MSE based MLP's by working within an extension of the well-formed cost functions framework of Wittner and Denker (1988). We present simulation examples to demonstrate this fact
    Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on; 06/1995
  • Source
    T. Adali, Xiao Liu, Ning Li, M.K. Sonmez
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    ABSTRACT: Presents the general formulation for adaptive equalization by distribution learning in which conditional probability mass function (PMF) of the transmitted signal given the received is parametrized by a general neural network structure. The parameters of the PMF are computed by minimization of the accumulated relative entropy (ARE) cost function. The equivalence of ARE minimization to maximum partial log-likelihood (MPLL) estimation is established under certain regularity conditions which enables the authors to bypass the requirement that the true conditionals be known. The large sample properties of MPLL estimator are obtained under further regularity conditions, and the binary case with sigmoidal perceptron as the conditional PMF model is shown to be a special case of the new framework. Results are presented which show that the multilayer perceptron (MLP) equalizer based on ARE minimization can always recover from convergence at the wrong extreme whereas the mean square error (MSE) based MLP can not
    Neural Networks for Signal Processing [1995] V. Proceedings of the 1995 IEEE Workshop; 01/1995
  • T. Adali, M.K. Sonmez
    [Show abstract] [Hide abstract]
    ABSTRACT: We formulate the adaptive channel equalization as a conditional probability distribution learning problem. Conditional probability density function of the transmitted signal given the received signal is parametrized by a sigmoidal perceptron. In this framework, we use relative entropy (Kullback-Leibler distance) between the true and the estimated distributions as the cost function to be minimized. The true probabilities are approximated by their stochastic estimators resulting in a stochastic relative entropy cost function. This function is well-formed in the sense of Wittner and Denker (1988), therefore gradient descent on this cost function is guaranteed to find a solution. The consistency and asymptotic normality of this learning scheme are shown via maximum partial likelihood estimation of logistic models. As a practical example, we demonstrate that the resulting algorithm successfully equalizes multipath channels
    Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on; 05/1994

Publication Stats

22 Citations
10.89 Total Impact Points

Institutions

  • 1994–1999
    • University of Maryland, Baltimore
      Baltimore, Maryland, United States