In this paper we address the problem of enhancing speech which has been degraded by additive noise. As proposed by Ephraim et al., autoregressive hidden Markov models (AR-HMM) for the clean speech and an autoregressive Gaussian for the noise are used. The filter applied to a given frame of noisy speech is estimated using the noise model and the autoregressive Gaussian having the highest a poste...

Autoregressive models are among the best performing neural density estimators. We describe an approach for increasing the flexibility of an autoregressive model, based on modelling the random numbers that the model uses internally when generating data. By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normaliz...

We introduce a general theory of epistemic random fuzzy sets for reasoning with or crisp evidence. This framework generalizes both the Dempster-Shafer belief functions, and possibility theory. Independent are combined by generalized product-intersection rule, which extends Dempster's rule combining product conjunctive combination distributions. Gaussian numbers their multi-dimensional extension...

we discuss in this paper the strong convergence for weighted sums of negatively orthant dependent (nod) random variables by generalized gaussian techniques. as a corollary, a cesaro law of large numbers of i.i.d. random variables is extended in nod setting by generalized gaussian techniques.

In many Monte Carlo simulations, it is important to generate samples from given densities. Recently, researchers in statistical signal processing and related disciplines have shown increased interest for a generator of random vectors with truncated multivariate normal probability density functions (pdf's). A straightforward method for their generation is to draw samples from the multivariate no...

Precise estimation of data distribution with a small number of sample patterns is an important and challenging problem in the field of statistical pattern recognition. In this paper, we propose a novel method for estimating multimodal data distribution based on the Gaussian mixture model. In the proposed method, multiple random vectors are generated after classifying the elements of the feature...

The multi-variate Gaussian distribution is used to model random processes with distinct pair-wise correlations, such as stock prices that tend to rise and fall together. Multi-variate Gaussian vectors with length n are usually produced by first generating a vector of n independent Gaussian samples, then multiplying with a correlation inducing matrix requiring O(n) multiplications. This paper pr...