5 sonuçlar
Arama Sonuçları
Listeleniyor 1 - 5 / 5
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 speech modeling method: SYMPES(IEEE, 2006) Güz, Ümit; Gürkan, Hakan; Yarman, Bekir Sıddık BinboğaIn this paper, the new method of speech modeling which is called SYMPES is introduced and it is compared with the commercially available methods. It is shown that for the same compression ratio or better, SYMPES yields considerably better hearing quality over the coders such as G.726 at 16 Kbps and voice excited LPC-10E of 2.4Kbps.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 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.
