Deniz Gençaga

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We present a novel and general methodology for modeling time-varying vector autoregressive processes which are widely used in many areas such as modeling of chemical processes, mobile communication channels and biomedical signals. In the literature, most work utilize multivariate Gaussian models for the mentioned applications, mainly due to the lack of(More)
In the last decade alpha-stable distributions have become a standard model for impulsive data. Especially the linear symmetric alpha-stable processes have found applications in various fields. When the process parameters are time-invariant, various techniques are available for estimation. However, time-invariance is an important restriction given that in(More)
In the literature, impulsive signals are mostly modeled by symmetric alpha-stable processes. To represent their temporal dependencies , usually autoregressive models with time-invariant coefficients are utilized. We propose a general sequential Bayesian modeling methodology where both unknown autoregressive coefficients and distribution parameters can be(More)
In a speech signal, Voice Onset Time (VOT) is the period between the release of a plosive and the onset of vocal cord vibrations in the production of the following sound. Voice Offset Time (VOFT), on the other hand, is the period between the end of a voiced sound and the release of the following plosive. Traditionally, VOT has been studied across multiple(More)
This paper addresses a problem that is of paramount importance in solving crimes wherein voice may be key evidence, or the only evidence: that of describing the perpetrator. The term Forensic anthropometry from voice refers to the deduction of the speaker's physical dimensions from voice. There are multiple studies in the literature that approach this(More)
In this work, we propose a novel approach to perform Dependent Component Analysis (DCA). DCA can be thought as the separation of latent, dependent sources from their observed mixtures which is a more realistic model than Independent Component Analysis (ICA) where the sources are assumed to be independent. In general, the sources can be spatio-temporally(More)
Information-theoretic quantities, such as entropy, are used to quantify the amount of information a given variable provides. Entropies can be used together to compute the mutual information, which quantifies the amount of information two variables share. However, accurately estimating these quantities from data is extremely challenging. We have developed a(More)
In this paper, we propose a novel approach to reduce the noise in Synthetic Aperture Radar (SAR) images using particle filters. Interpretation of SAR images is a difficult problem, since they are contaminated with a multiplicative noise, which is known as the " Speckle Noise ". In literature, the general approach for removing the speckle is to use the local(More)
In this work, a new method is proposed in order to sequentially estimate the time-varying parameters of a Cauchy distributed process. For this purpose, particle filters, which are used in non-Gaussian and nonlinear Bayesian applications, are utilised. The proposed method forms a basis for the possible future applications of the-stable distributions with(More)