Düzenli̇, Timur

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Profile URL
https://hdl.handle.net/20.500.14365/7439
Name Variants
Duzenli, Timur
Düzenli̇, Timur
Düzenlı, Timur
Job Title
Email Address
timur.duzenli@ieu.edu.tr
Main Affiliation
05.06. Electrical and Electronics Engineering
Status
Former Staff
Website
ORCID ID
https://orcid.org/0000-0003-0210-5626 View this author’s ORCID profile
Scopus Author ID
36975195100
Turkish CoHE Profile ID
C018B553CDBE6724
Google Scholar ID
SCdcfqYAAAAJ
WoS Researcher ID
CQF-2196-2022

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21

Citations

229

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19

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Scholarly Output

3

Articles

2

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0/0

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0

Supervised PhD Theses

0

WoS Citation Count

2

Scopus Citation Count

7

WoS h-index

1

Scopus h-index

2

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0

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0

WoS Citations per Publication

0.67

Scopus Citations per Publication

2.33

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1

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0

JournalCount
2010 National Conference on Electrical, Electronics and Computer Engineering, ELECO 20101
ISRN Signal Processing1
Istanbul Unıversıty-Journal of Electrıcal And Electronıcs Engıneerıng1
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Now showing 1 - 3 of 3
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 2
    Comparison of Wavelet Based Feature Extraction Methods for Speech/Music Discrimination [conference Object]
    (2010) Düzenli T.; Özkurt N.
    In this study, performance of wavelet transform based features for the speech / music discrimination task has been investigated. In order to extract wavelet domain features, discrete and complex wavelet transforms have been used. The performance of the proposed feature set has been compared with a feature set constructed from the most common time, frequency and cepstral domain features used in speech/music discrimination such as number of zero crossings, spectral centroid, spectral flux and mel cepstral coefficients. In order to measure the performances of the feature sets for the speech/music discrimination, artificial neural networks have been used as a classification tool. The principal component analysis has been applied to eliminate the correlated features before classification stage. Considering the number of vanishing moments and orthogonality, the best performance is obtained with Daubechies8 wavelet among the other members of the Daubechies family. According to the results the proposed feature set outperforms the traditional ones.
  • Thumbnail Image
    Article
    Citation - Scopus: 2
    Discrete and Dual Tree Wavelet Features for Real-Time Speech/Music Discrimination
    (Hindawi Limited, 2011) Düzenli T.; Özkurt N.
    The performance of wavelet transform-based features for the speech/music discrimination task has been investigated. In order to extract wavelet domain features, discrete and complex orthogonal wavelet transforms have been used. The performance of the proposed feature set has been compared with a feature set constructed from the most common time, frequency and cepstral domain features such as number of zero crossings, spectral centroid, spectral flux, and Mel cepstral coefficients. The artificial neural networks have been used as classification tool. The principal component analysis has been applied to eliminate the correlated features before the classification stage. For discrete wavelet transform, considering the number of vanishing moments and orthogonality, the best performance is obtained with Daubechies8 wavelet among the other members of the Daubechies family. The dual tree wavelet transform has also demonstrated a successful performance both in terms of accuracy and time consumption. Finally, a real-time discrimination system has been implemented using the Daubhecies8 wavelet which has the best accuracy. Copyright © 2011 T. Düzenli and N. Ozkurt.
  • Thumbnail Image
    Article
    Citation - WoS: 2
    Citation - Scopus: 3
    Comparison of Wavelet Based Feature Extraction Methods for Speech/Music Discrimination [article]
    (Istanbul Univ, Fac Engineering, 2011) Duzenli, Timur; Ozkurt, Nalan
    The speech/music discrimination systems have gaining importance in several intelligent audio retrieval algorithms due to the increasing size of the multimedia sources in our daily lives. This study aims to propose a speech/music discrimination system which utilizes the advantages of the wavelet transform. Also, the performance of the discrete wavelet transform and the dual-tree wavelet transform has been compared with the conventional time, frequency and cepstral domain features used in speech/music discrimination. The speech and music samples collected from common databases, CD recording and internet radios have been classified with artificial neural networks with different feature sets. The principal component analysis has been applied to eliminate the correlated features before classification stage. Considering the number of vanishing moments and orthogonality, the best performance has been obtained with Daubechies8 wavelet among the other members of the Daubechies family. According to the results, the proposed feature set outperforms the traditional ones.