Browsing by Author "Çetin, A.E."
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Item Open Access A complexity-reduced ML parametric signal reconstruction method(2011) Deprem, Z.; Leblebicioglu, K.; Arkan O.; Çetin, A.E.The problem of component estimation from a multicomponent signal in additive white Gaussian noise is considered. A parametric ML approach, where all components are represented as a multiplication of a polynomial amplitude and polynomial phase term, is used. The formulated optimization problem is solved via nonlinear iterative techniques and the amplitude and phase parameters for all components are reconstructed. The initial amplitude and the phase parameters are obtained via time-frequency techniques. An alternative method, which iterates amplitude and phase parameters separately, is proposed. The proposed method reduces the computational complexity and convergence time significantly. Furthermore, by using the proposed method together with Expectation Maximization (EM) approach, better reconstruction error level is obtained at low SNR. Though the proposed method reduces the computations significantly, it does not guarantee global optimum. As is known, these types of non-linear optimization algorithms converge to local minimum and do not guarantee global optimum. The global optimum is initialization dependent. © 2011 Z. Deprem et al.Item Open Access Line spectral frequency representation of subbands for speech recognition(1995) Erzin, E.; Çetin, A.E.In this paper, a new set of speech feature parameters is constructed from subband analysis based Line Spectral Frequencies (LSFs). The speech signal is divided into several subbands and the resulting subsignals are represented by LSFs. The performance of the new speech feature parameters, SUBLSFs, is compared with the widely used Mel Scale Cepstral Coefficients (MELCEPs). SUBLSFs are observed to be more robust than the MELCEPs in the presence of car noise. © 1995.Item Open Access Restoration of error-diffused images using projection onto convex sets(2001) Unal G.B.; Çetin, A.E.In this paper, a novel inverse halftoning method is proposed to restore a continuous tone image from a given half-tone image. A set theoretic formulation is used where three sets are defined using the prior information about the problem. A new space-domain projection is introduced assuming the halftoning is performed using error diffusion, and the error diffusion filter kernel is known. The space-domain, frequency-domain, and space-scale domain projections are used alternately to obtain a feasible solution for the inverse halftoning problem which does not have a unique solution.