Deniz Gençaga

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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)
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)
—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)
Information-theoretic quantities, such as entropy and mutual information (MI), can be used to quantify the amount of information needed to describe a dataset or the information shared between two datasets. In the case of a dynamical system, the behavior of the relevant variables can be tightly coupled, such that information about one variable at a given(More)
In adaptive signal processing, joint process estimation plays an important role in various estimation problems. It is well known that a joint process estimator consists of two structures , namely the orthogonalizer and the regression filter. In literature, orthogonalization step is performed either by orthogonal transformations or by linear predictors.(More)
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