Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği

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

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Author: search.filters.author.Özkurt, NalanSubject: search.filters.subject.Wavelet ridge

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  • Article
    Citation - WoS: 8
    Citation - Scopus: 8
    Wavelet Ridges for Musical Instrument Classification
    (Springer Verlag, 2012) Özbek, Mehmet Erdal; Özkurt, Nalan; Savacı, Ferit Acar
    The time-varying frequency structure of musical signals have been analyzed using wavelets by either extracting the instantaneous frequency of signals or building features from the energies of sub-band coefficients. We propose to benefit from a combination of these two approaches and use the time-frequency domain energy localization curves, called as wavelet ridges, in order to build features for classification of musical instrument sounds. We evaluated the representative capability of our feature in different musical instrument classification problems using support vector machine classifiers. The comparison with the features based on parameterizing the wavelet sub-band energies confirmed the effectiveness of the proposed feature. © 2011 Springer Science+Business Media, LLC.
  • Article
    Citation - WoS: 21
    Citation - Scopus: 37
    Determination of Wavelet Ridges of Nonstationary Signals by Singular Value Decomposition
    (Institute of Electrical and Electronics Engineers Inc., 2005) Özkurt, Nalan; Savacı, Ferit Acar
    The ridges obtained from chaotic signals can give the relevant information about the phase structures of the dynamical systems. Therefore, a new wavelet ridge determination method for the noisy signals and nonstationary signals, which is based on the singular value decomposition (SVD) has been proposed in this paper. The proposed method has been compared with Carmona method for monocomponent signals, and multicomponent signals. The proposed method is computationally more effective than the Carmona method to determine the actual ridges. Also, the ridges of the periodic limit cycles and chaotic attractors have been determined by using the SVD-based method to find the degree of chaoticity.