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Yayın Generation of optimum signature base sequences for speech signals(IEEE, 2000) Yarman, Bekir Sıddık Binboğa; Akdeniz, RafetIn our previous publications [1-6], we proposed a novel method to represent signals in terms of, so called, "Signature Base Functions-SBF' which were extracted from the physical features of the waveform under consideration. In [1-6], SBF were determined in ad-hoc manner, which requires tedious search process, and they were not orthogonal. Furthermore, optimality of SBF was in question. In this work however, we suggest a well-organised procedure to generate "Optimum Orthogonal Signature Base Functions-OSBF' for selected waveforms, which in turn provides excellent means for signal representations. II is shown that the new method of signal representation, which is based on OSBF, requires less computation time with substantial signal compression and results in efficient speaker dependent recognition.Yayın Representation of speech signals by single signature base function within optimum frame length(IEEE, 2000) Akdeniz, Rafet; Yarman, Bekir Sıddık BinboğaBefore this study, we proposed a novel method to represent signals in terms of, so called, “Signature Base Functions-SBF" which were extracted from the physical features of the waveform under consideration. SBF were determined in ad-hoc manner, which requires tedious search process, and they were not orthogonal. Furthermore, optimality of SBF was in question. In this work however, we suggest a well-organized procedure to generate “Optimum Orthogonal Signature Base Functions-OSBF" for selected waveforms, which in turn provides excellent means for signal representations. It is shown that the new method of signal representation, which is based on OSBF, requires less computation time with substantial signal compression and results in efficient speaker dependent recognition.Yayın A new algorithm for high speed speech and audio coding(IEEE, 2007) Güz, Ümit; Gürkan, Hakan; Yarman, Bekir Sıddık BinboğaIn this work, a new mathematical modeling approach is proposed for the representation of the speech and audio signals. This approach is based on the generation of the so called Predefined Signature Sequence (PSS) and Predefined Envelope Sequence (PES) Sets. After the generation process of the PSS and PES sets, they are clustered by effective k-means clustering algorithm and the PSS and PES are redefined by using the centroids of the clusters. By using this approach, the drawbacks such as the size of the sets, speed of the reconstruction process (computational complexity) which arise in our proposed methods previously are highly eliminated. In spite of these improvements, the initial results proved that, the quality of the reconstructed signals remains within the limitations of the acceptable hearing quality.Yayın A new coding method for speech and audio signals(IEEE, 2005) Güz, Ümit; Gürkan, Hakan; Yarman, Bekir Sıddık BinboğaIn this paper a new representation or modeling method of speech signals is introduced. The proposed method is based on the generation of the so-called Predefined Signature S={S R } and Envelope vector E={E K } Sets (PSEVS). These vector sets are speaker and language independent. In this method, once the speech signals are divided into frames with selected lengths, then each frame signal piece X i is reconstructed by means of the mathematical form of X i =C i E K S R . In this representation, C i is called the frame coefficient, S R and E K are the vectors properly assigned from the PSEVS respectively. It is shown that the proposed method provides fast reconstruction and substantial compression ratio with acceptable hearing quality.Yayın Elektroensefalogram (EEG) işaretlerinin sıkıştırılmasında özgün bir yaklaşım(IEEE, 2008) Gürkan, Hakan; Güz, Ümit; Yarman, Bekir Sıddık BinboğaBu çalışmada, Elektroensefalogram(EEG) işaretlerinin yeniden oluşturulmasına yönelik olarak yeni bir yöntem sunulmaktadır. Sunulan yöntem, etkin bir k-ortalamalı sınıflandırma algoritması kullanılarak Sınıflandırılmış Temel Tanım ve Zarf Vektör Setlerinin oluşturulmasına dayanmaktadır. Bu çalışmada, EEG işaretleri eşit uzunluklu çerçevelere bölünerek analiz edilmiş ve herbir çerçeve Sınıflandırılmış Temel Tanım vektörü, Sınıflandırılmış Zarf vektörü ve Çerçeve Ölçekleme Katsayısı olarak adlandırılan üç parametrenin çarpımı biçiminde modellenmiştir. Bu durumda, EEG işaretinin herbir çerçevesi sınıflandırılmış temel tanım ve zarf vektör setlerine ilişkin iki sıra numarası R ve K ile çerçeve ölçekleme katsayısı cinsinden tanımlanabilir. Önerilen yöntemin başarımı ortalama karesel hata tanımı ve görsel inceleme ölçütü yoluyla değerlendirilmiştir. Önerilen yöntem, EEG işaretlerinin tanı açısından önemli kısımları korunarak, düşük yeniden oluşturma hataları ve yüksek sıkıştırma oranları ile yeniden oluşturulmasını sağlamaktadır.Yayın A novel method to represent the speech signals by using language and speaker independent predefined functions sets(IEEE, 2004) Güz, Ümit; Gürkan, Hakan; Yarman, Bekir Sıddık BinboğaIn this paper a new modeling method of speech signals is introduced. The proposed method is based on the generation of the so-called Predefined Signature S={s(R)(t)} and Envelope Function E = {e(K)(t)} Sets (PSEFS). These function sets are independent of any speaker and any language. Once the speech signals are divided into frames with selected lengths, then each frame signal piece X-i(t) is synthesized by means of the mathematical form of x(i)(t)=C(i)e(K)(t)s(R)(t). In this representation, C-i is called the frame coefficient, s(R)(t) and e(K)(t) are properly assigned from the PSEFS respectively. It is shown that the proposed method provides fast reconstruction and substantial compression with acceptable hearing quality.Yayın A novel representation method for electromyogram (EMG) signal with predefined signature and envelope functional bank(IEEE, 2004) Gürkan, Hakan; Güz, Ümit; Yarman, Bekir Sıddık BinboğaIn this paper, a new method to model EMG signals by means of "Predefined Signature and Envelope Functional Banks (PSEB)" is presented. Since EMG signals present quasi-stationary behavior, any EMG signal Xi is modeled by the form of Xi ? Ci?K?R on a frame bases in this work. In this model, ?R is defined as the Predefined Signature Vector (PSV); ?K is referred to as Predefined Envelope Vector (PEV) and Ci is called the Frame-Scaling Coefficient (FSC). EMG signal for each frame is described in terms of the two indices "R" and "K" of PSEB and the frame -scaling coefficient Ci. Furthermore, It has been shown that the new method of modeling provides significant data compression while preserving the clinical information in the reconstructed signal.Yayın EEG signal compression based on classified signature and envelope vector sets(IEEE Computer Society, 2007) Gürkan, Hakan; Güz, Ümit; Yarman, Bekir Sıddık BinboğaIn this paper, a novel method to compress ElectroEncephaloGram (EEG) Signal is proposed. The proposed method is based on the generation Classified Signature and Envelope Vector Sets (CSEVS) by using an effective k-means clustering algorithm. In this work on a frame basis, any EEG signal is modeled by multiplying three parameters as called the Classified Signature Vector, Classified Envelope Vector, and Frame-Scaling Coefficient. In this case, EEG signal for each frame is described in terms of the two indices R and K of CSEVS and the frame-scaling coefficient. The proposed method is assessed through the use of root-mean-square error (RMSE) and visual inspection measures. The proposed method achieves good compression ratios with low level reconstruction error while preserving diagnostic information in the reconstructed EEG signal.
