انجمن کامپیوتر ایران

This paper describes the theory and implementation of dynamic Bayesian networks in the context of speaker identification. Dynamic Bayesian networks provide a succinct and expressive graphical language for factoring joint probability distributions, and we begin by presenting the structures that are appropriate for doing speaker identification in clean and noisy environments. This approach is notable because it expresses an identification system using only the concepts of random variables and conditional probabilities. We present illustrative experiments in both clean and noisy environments and our experiments show that this new approach is very promising in the field of speaker identification.

In this paper a two step method based on Gauss- Newton and factor graphs algorithm is proposed for localization to enhance accuracy of localization. The Gauss-Newton algorithm is accurate method for positioning. The most important challenge of this method is senility to initial point; this problem is solved in positioning based on factor graphs. So, in this paper, first positioning equations using angle of arrival is considered based on factor graphs algorithm. Second, final location estimation is performed using Gauss Newton algorithm with error near to Cramer-Rao bound. Simulation results shows that positioning error using two step method has maximum 6% gap to Cramer-Rao Bound.