Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/11

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Author: search.filters.author.Çek, Mehmet EmreScopus Q: search.filters.scopusQuartile.Q2

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  • Article
    Citation - WoS: 3
    Citation - Scopus: 4
    Stochastic bifurcation in generalized chua's circuit driven by skew-normal distributed noise
    (World Scientific Publishing Co. Pte Ltd, 2018) Yılmaz, Serpil; Çek, Mehmet Emre; Savacı, Ferit Acar
    In this study, the stochastic phenomenological bifurcations (P-bifurcations) of generalized Chua's circuit (GCC) driven by skew-normal distributed noise have been investigated by numerically obtaining the stationary distributions of the stochastic responses. The noise intensity and/or skewness parameters of skew-normal distributed noise have been chosen as the bifurcation parameters to change the structure of the stochastic attractor. While the number of breakpoints in the piecewise-linear characteristics of the GCC are fixed, it has been observed that the number of scrolls have been changed by tuning the noise intensity and the skewness parameter of the skew-normal distributed noise.
  • Article
    Skewed Alpha-Stable Distributions for Modeling and Classification of Musical Instruments
    (Türkiye Klinikleri Journal of Medical Sciences, 2012) Özbek, Mehmet Erdal; Çek, Mehmet Emre; Savacı, Ferit Acar
    Music information retrieval and particularly musical instrument classification has become a very popular research area for the last few decades. Although in the literature many feature sets have been proposed to represent the musical instrument sounds, there is still need to find a superior feature set to achieve better classification performance. In this paper, we propose to use the parameters of skewed alpha-stable distribution of sub-band wavelet coefficients of musical sounds as features and show the effectiveness of this new feature set for musical instrument classification. We compare the classification performance with the features constructed from the parameters of generalized Gaussian density and some of the state-of-the-art features using support vector machine classifiers.