Özaydın, Selma
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Dr. Öğr. Üyesi
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Çankaya Meslek Yüksekokulu
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Scholarly Output
17
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20
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20
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0
17 results
Scholarly Output Search Results
Now showing 1 - 10 of 17
Conference Object Citation - WoS: 4Citation - Scopus: 11Design of a text ındependent speaker recognition system(Ieee, 2017) Ozaydin, Selma; Özaydın, Selma; Ozaydin, Selma; 253019; Çankaya Meslek YüksekokuluThis paper presents the design of a text independent speaker recognition system based on Mel-Frequency Cepstrum Coefficients and Gaussian Mixture Models. HTK speech recognition toolkit is used in the design of speaker models. The system is aimed to use it as a biometric authentication system. The experiments were performed on speech data consist of 134 speakers from YOHO database for different training conditions. The increase of the proposed system performance is observed with the decrease of Equal Error Rate. Experiment results show that the system gives the best recognition performance for Gaussian mixture model with 64 mixtures.Article Comparative Analysis of Early Studies on Turkish Whistle Language and a Case Study on Test Conditions(2018) Özaydın, Selma; 253019; Çankaya Meslek YüksekokuluThis paper examines the early studies on Turkish Whistle Language and it argues that they have some controversial results. The study considers these issues need to be discussed and searched again in terms of linguistic and phonetic form. Unfortunately, there are few research studies on Turkish whistle language and most of them were performed nearly fifty years ago despite the fact that this language still has been used in Kuskoy region. Therefore, the findings of these early research studies could give valuable information to start to a new research study on the subject. The first scientific study on Turkish Whistle language was performed by a French scientist R.G. Busnel with his multidisciplinary research team in 1967. Some of this research’s results were published in the book. A Turkish scientist O. Baskan also participated in Busnel’s research group and published a paper on TWsL in 1968. However, some assertions such as people having a tendency to understand the Turkish whistle language with three vowels and three consonants have not been in the research results of R.G. Busnel. In addition, a Turkish scientist D. Aksan in Turkey had performed another research at Kuskoy region with his own team. Their test methods were different from the previous ones. This paper analyzes these research results on a comparative basis and presents the common and conflicted issues to discuss the uncertain points. The comparative evaluation of these past research studies aims to highlight the controversial position of the results on Turkish Whistle Language. In this scope, this paper opens a discussion about the selection of test conditions for an acoustic and linguistic analysis of the Turkish whistle language.Article Citation - WoS: 0Citation - Scopus: 2Comparative Performance Analysis of Filtering Methods for Removing Baseline Wander Noise from an ECG Signal(World Scientific Publ Co Pte Ltd, 2024) Ozaydin, Selma; Özaydın, Selma; Ahmad, Imteyaz; 253019; Çankaya Meslek YüksekokuluECG signals play a vital role in the diagnosis of cardiovascular conditions. However, they often suffer from the effects of various noise sources, including baseline wandering, respiratory artifact noise, power line interference and electrode motion artifacts. To overcome these challenges, it is imperative to implement low-frequency signal noise reduction strategies. Such strategies aim to significantly improve the quality of ECG signals, thus promoting more accurate and reliable diagnosis of cardiovascular disorders. This paper conducts a comparative analysis to assess the effectiveness of commonly used filtering and wavelet techniques in reducing Baseline Wander (BW) noise within ECG signals generated by the influence of breathing or electrode movements. It is common to observe the selection and evaluation of only one particular technique in the existing literature. In contrast, this study aims to provide a comprehensive comparative analysis, providing insight into the performance and relative merits of different techniques. Our research uses both filtering and Discrete Wavelet Transform (DWT) techniques in baseline noise removal. In this context, a reference point is established utilizing noise-free signals and a meticulous investigation of the wavelet-based approach that most effectively eliminates the resulting noise is provided. Subsequently, we assess the reference input and output signal via Signal-to-Noise Ratio (SNR) and Kolmogorov-Smirnov statistical test measurements. The most important contribution of this work to the scientific community resides in the comprehensive examination of IIR/FIR-based and wavelet method-based filtering methods capable of yielding the highest SNR levels across various ECG signals with various types of BW noise. Additionally, the effectiveness of the Chebychev-II filter in BW noise removal is highlighted. Our study was conducted using the MATLAB platform and code command lines were shared to facilitate the reproduction of our study by other researchers. It is considered that this study will be an important reference in the selection of effective techniques for removing BW noise within ECG signals.Conference Object A complexity reduction method for joint MSVQ(2016) Özaydın, Selma; Çankaya Meslek YüksekokuluArticle Examination of Energy Based Voice Activity Detection Algorithms for Noisy Speech Signals(2019) Özaydın, Selma; Ozaydin, Selma; 253019; Çankaya Meslek YüksekokuluBu çalışmada, iki farklı enerji tabanlı konuşma bölgesi aktivasyonu detektör (KAD) algoritmasının gürültülü giriş sinyallerine karşı davranışları incelenmektedir. İncelenen KAD detektörleri, konuşma sınırlarını etkin bir şekilde belirlemek için zaman düzlemindeki metotları kullanmaktadır. Zaman düzlemi kısa zaman aralığında enerji hesabı ve/veya sıfır geçiş oranı, metotların performansını değerlendirmede kullanılmaktadır. Her iki algoritmanın ilk aşamasında, zaman düzleminde her bir konuşma alt kesitinde enerji değerleri hesaplanmaktadır. Enerji oranları ve eşik değerler, konuşma sinyalinin aktif bölgelerini belirlemede kullanılmaktadır. Karar eşik değeri, konuşma sinyalinin başında sessiz bir bölge aralığında hesaplanmaktadır. Seçilen metotların etkinliği temiz ve gürültülü konuşma sinyal örnekleri için test edilmiştir. Metotlar, değişik SNR seviyelerinde gürültülü konuşma sinyalleri kullanarak test edilmiştir. Sonuçlar göstermiştir ki, 0dB SNR seviyesine kadar yavaşca azalan performansla her iki metot etkinliklerini koruyabilmekte, ancak 0dB SNR seviyesi altında her iki metot etkinliğini kaybetmektedir.Conference Object Citation - Scopus: 5Speech Denoising with Maximal Overlap Discrete Wavelet Transform(Institute of Electrical and Electronics Engineers Inc., 2022) Alak, I.K.; Özaydın, Selma; Ozaydin, S.; 253019; Çankaya Meslek YüksekokuluIn this paper, the effectiveness of the maximum overlapping discrete wavelet transform (MODWT) method on denoising the speech signal is tested and examined. Ensuring the intelligibility of the speech signal in noisy environments by separating it from the noise is a widely researched topic today. On the other hand, being able to recover the original speech from the noisy signal with minimal distortion is a challenge due to the difficulties in removing the background noise. Numerous factors in environmental noise environments can interfere with the signal. In this study, the performance of some discrete wavelets transform methods is experimentally analyzed using different wavelet filters. The analysis program was carried out in the MATLAB environment. As the input noise speech signal, speech sounds containing different environmental background noises (train, car, station, plane, etc.) were analyzed. During the tests, these noisy input signals were filtered out from the speech signal by wavelet analysis. The input noisy speech signal is decomposed into wavelet coefficients with different thresholding methods. The reconstructed speech was compared by measuring the signal-to-noise ratio (SNR) values between the noisy input signal and the smoothed output signals. The scientific contributions of the study include a detailed comparative analysis of the performances of various wavelet methods against different background environmental noises. © 2022 IEEE.Conference Object Citation - Scopus: 1Design of a Voice Activity Detection Algorithm based on Logarithmic Signal Energy(Institute of Electrical and Electronics Engineers Inc., 2022) Ozaydin, S.; Özaydın, Selma; 253019; Çankaya Meslek YüksekokuluThis article presents a new method for calculating the signal energies of speech segments in voice activity detection algorithms. In the study, the μ-law signal compression method is adapted to calculate short-term signal energies. A simple voice activity detection (VAD) algorithm is designed to demonstrate the effectiveness of the proposed method. The same VAD algorithm was also run with two different conventional energy calculation formulas and the performance of each VAD was evaluated using time-domain short-time energy features. The G729 standard VAD algorithm was also used for performance comparison. During the test of the analyzed detectors, many kinds of input speech signals with various types of background environmental noise, such as restaurants, vehicles, and streets, were tested. Using the new energy calculation method, the VAD detector has improved detection accuracy compared to VAD detectors based on the other two energy methods and was able to effectively identify voice-active regions even in noisy conditions at low SNR levels. The results revealed that the VAD detector designed with the proposed new energy calculation formula outperforms traditional energy-based voice activity detection methods and provides noticeable increases in detection rate even under adverse conditions. © 2022 IEEE.Article Comparative Analysis of Early Studies on Turkish Whistle Language and a Case Study on Test Conditions(2018) Özaydın, Selma; 253019; Çankaya Meslek YüksekokuluThis paper examines the early studies on Turkish Whistle Language and it argues that they have some controversial results. The study considers these issues need to be discussed and searched again in terms of linguistic and phonetic form. Unfortunately, there are few research studies on Turkish whistle language and most of them were performed nearly fifty years ago despite the fact that this language still has been used in Kuskoy region. Therefore, the findings of these early research studies could give valuable information to start to a new research study on the subject. The first scientific study on Turkish Whistle language was performed by a French scientist R.G. Busnel with his multidisciplinary research team in 1967. Some of this research’s results were published in the book. A Turkish scientist O. Baskan also participated in Busnel’s research group and published a paper on TWsL in 1968. However, some assertions such as people having a tendency to understand the Turkish whistle language with three vowels and three consonants have not been in the research results of R.G. Busnel. In addition, a Turkish scientist D. Aksan in Turkey had performed another research at Kuskoy region with his own team. Their test methods were different from the previous ones. This paper analyzes these research results on a comparative basis and presents the common and conflicted issues to discuss the uncertain points. The comparative evaluation of these past research studies aims to highlight the controversial position of the results on Turkish Whistle Language. In this scope, this paper opens a discussion about the selection of test conditions for an acoustic and linguistic analysis of the Turkish whistle language.Conference Object Citation - Scopus: 0A Graphical Speech Analysis Teaching Tool(Institute of Electrical and Electronics Engineers Inc., 2022) Ozaydin, S.; Özaydın, Selma; 253019; Çankaya Meslek YüksekokuluThe widespread use of digital speech processing in today's technologies causes many electronics and computer engineering students to need a basic background in these subjects. The paper describes a toolbox designed to support undergraduate or graduate level courses on speech processing. The proposed educational toolbox is designed as a virtual lab for basic operations in digital speech processing-based courses. This graphical user interface (GUI) based speech analysis algorithm is built with six main function modules, which are signal input, noise addition, up-sampling/down-sampling, time domain feature analysis, pitch detection and frequency domain analysis. The toolbox involves different operations for measuring important speech feature parameters such as pitch, energy, zero-crossing ratio, FFT and power spectrum of an input speech signal. The toolbox has also been developed to easily manipulate and add some other possible speech processing methods. It is thought that the tool will make it easier for students to understand the methods that form the basis of digital speech processing, increase the interest in the lesson with its visual outputs, and allow new methods to be added easily when desired thanks to its simple and modular structure. The main aim of this paper to show how such a tool facilitates students understanding of technical concepts introduced in speech courses. © 2022 IEEE.Article Citation - WoS: 0Citation - Scopus: 0Investigation of factors affecting noise robustness in voice activity detectors(Gazi Univ, Fac Engineering Architecture, 2023) Ozaydin, Selma; Özaydın, Selma; 253019; Çankaya Meslek YüksekokuluIn this manuscript, some voice activity detectors (VADs) in the literature were examined in terms of factors affecting their robustness under different acoustic noise conditions and in this context, the changes in detection accuracy rates according to changing noise conditions were tested. In this scope, the effect of situations such as whether the threshold value used in the decision phase in VAD methods is fixed or adaptive, the analysis window is short or long, the use of more than one feature vector together has been evaluated and analyzed comparatively. While three of the four different VAD detectors examined in this manuscript use feature vectors within the short-term analysis window while generating the decision result, one decides according to the measurement result of long-term spectral vectors. The VAD detectors in the article have been tested using the NOIZEUS noisy speech database. Thus, the performance of the analyzed VADs has been evaluated under different acoustic conditions using an extensive database that has already taken place in the literature. During the testing of the analyzed VADs, different input noise speech signals with environmental background noises between [15-0dB] such as restaurant, car, street, or station were tested. Tests were carried out using objective test measurement methods and the detection accuracy rate of each VAD method was measured. The results showed that each method gave different endurance performance in adverse environmental conditions.
