Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Article Citation - WoS: 18Citation - Scopus: 31Applied Mel-Frequency Discrete Wavelet Coefficients and Parallel Model Compensation for Noise-Robust Speech Recognition(Elsevier, 2006) Tüfekçi, Zekeriya; Gowdy, John N.; Gürbüz, Sabri; Patterson, EricInterfering noise severely degrades the performance of a speech recognition system. The Parallel Model Compensation (PMC) technique is one of the most efficient techniques for dealing with such noise. Another approach is to use features local in the frequency domain, such as Mel-Frequency Discrete Wavelet Coefficients (MFDWCs). In this paper, we investigate the use of PMC and MFDWC features to take advantage of both noise compensation and local features (MFDWCs) to decrease the effect of noise on recognition performance. We also introduce a practical weighting technique based on the noise level of each coefficient. We evaluate the performance of several wavelet-schemes using the NOISEX-92 database for various noise types and noise levels. Finally, we compare the performance of these versus Mel-Frequency Cepstral Coefficients (MFCCs), both using PMC. Experimental results show significant performance improvements for MFDWCs versus MFCCs, particularly after compensating the HMMs using the PMC technique. The best feature vector among the six MFDWCs we tried gave 13.72 and 5.29 points performance improvement, on the average, over MFCCs for -6 and 0 dB SNR, respectively. This corresponds to 39.9% and 62.8% error reductions, respectively. Weighting the partial score of each coefficient based on the noise level further improves the performance. The average error rates for the best MFDWCs dropped from 19.57% to 16.71% and from 3.14% to 2.14% for -6 dB and 0 dB noise levels, respectively, using the weighting scheme. These improvements correspond to 14.6% and 31.8% error reductions for -6 dB and 0 dB noise levels, respectively. (c) 2006 Elsevier B.V. All rights reserved.Article Citation - WoS: 4Citation - Scopus: 5The Implementation of Nonlinear Dynamical Systems With Wavelet Network(Urban und Fischer Verlag GmbH und Co. KG, 2006) Özkurt, Nalan; Savacı, Ferit AcarA dynamic wavelet network circuit implementation for modelling the nonlinear dynamical networks has been proposed in this study. The dynamical wavelet network includes static wavelet network with Mexican hat wavelet function, the voltage-controlled switches and capacitors. The circuit simulations have been done in Spice for the period-1 limit cycle, the spiral and double scroll attractors of the Chua's circuit.Article Citation - WoS: 2Citation - Scopus: 3The System Modelling and Circuit Implementation From Time-Frequency Domain Signal Specifications(Urban und Fischer Verlag GmbH und Co. KG, 2008) Savacı, Ferit Acar; Özkurt, NalanThe system modelling and the circuit implementation of the nonlinear circuits using the wavelet domain techniques has been accomplished in this study. When the time-frequency domain specifications have been given as the wavelet ridges, the signal with the given ridges has been synthesized. Then, the dynamical wavelet network has been trained for the synthesized signal. The circuit of the wavelet network has been designed and simulated. © 2007 Elsevier GmbH. All rights reserved.Article Citation - WoS: 14Citation - Scopus: 14The Circuit Implementation of a Wavelet Function Approximator(Springer Verlag, 2002) Özkurt, Nalan; Savacı, Ferit Acar; Gündüzalp, MustafaThis paper describes the analog synthesis of a wavelet function approximator using sigmoidal mother wavelet. Any finite energy multivariate function can be approximated by this analog circuit using the multiresolution approximation property of the wavelet decomposition. The approximator circuit includes bipolar junction transistors, operational amplifiers and linear passive circuit elements.