Copyright © 2013 IJECCE, All right reserved 1180 International Journal of Electronics Communication and Computer Engineering Volume 4, Issue 4, ISSN (Online): 2249–071X, ISSN (Print): 2278–4209 The Effect of Dolph-Chebyshev Window Side Lobe Attenuation on Speech Enhancement using Weiner Filter Method S. China Venkateswarlu R S, JNTUK-Kakinada A. SubbaRami Reddy Dept. of ECE, SKIT, Srikalahasti K. Satya Prasad Dept. of ECE JNTUK-Kakinada Abstract – This paper investigates the effect of Dolph- Chebyshev window frequency response Side lobe Attenuation on the improvement of Speech quality in terms of six objective quality measures. In Speech Enhancement process, signal corrupted by noise is segmented into frames and each segment is Windowed using Dolph-Chebyshev Window with variation in the side lobe attenuation parameter α. The Windowed Speech segments are applied to the Weiner Filter Speech Enhancement algorithm and the Enhanced Speech signal is reconstructed in its time domain. The focus is to study the effect of Dolph-Chebyshev Window spectral side lobe attenuation on the Speech Enhancement process. For different side lobe attenuations of the Dolph-Chebyshev Window frequency response, speech quality objective measures have been computed. From this study, it is observed that the Side lobe Attenuation parameter α plays an important role on the Speech enhancement process in terms of six objective quality measures. The results are compared with the measures of Hanning window and an optimum side lobe attenuation parameter in dB for the Dolph-Chebyshev Window is proposed for better speech quality. Keywords – DFT, Dolph-Chebyshev Window, Objective Measures, Speech Enhancement, Side Lobe Attenuation. I. INTRODUCTION Speech enhancement is the most important field of speech processing. It is used for many applications such as mobile phones, teleconferencing systems, speech recognition and hearing aids. The processed speech signals should be more comfort for listening and also should give better performance in tasks like automatic speech and speaker recognition [1]. Several algorithms are proposed in the literature [2] for speech enhancement such as spectral subtraction methods, MMSE methods, Weiner algorithm etc. In Weiner Filter Method based speech enhancement method, the noisy speech signal is partitioned into frames. Each frame is multiplied by a window function prior to the spectral analysis and applied to the speech enhancement algorithm. The focus of the study is on the effect of windowing in the Speech Enhancement process in terms of six objective measures for Weiner Filter Method Speech Enhancement process. The purpose of windowing is to reduce the effect of discontinuity introduced by the framing process. Commonly used windows include Hamming and Hanning [3]. Although these windows have a reduced side lobe levels they have also reduced frequency resolution. Hence several factors enter into the choice of Window selection to frame the Speech for Enhancement. In this paper an attempt has been made to explore the possibility of improving the quality of speech signal by employing different shapes of Dolph-Chebyshev Window. To study the performance of any algorithm, combinations of subjective and objective measures have to be carried on. Currently, the accurate method for evaluating speech quality is through subjective listening tests. But it is costly and time consuming. Hence, six objective measures are chosen to evaluate the performance of the Dolph- Chebyshev Window in an enhancement system. P.Loizou has presented a correlation analysis of objective quality measures for evaluating speech enhancement algorithms [4]. In this paper six measures namely SNR, Segmental SNR(Seg-SNR), Log Likelihood Ratio(LLR), Weighted spectral slope distance(WSS), frequency weighted segmental SNR (fwseg-SNR) and Cepstral distance (Cep) are selected for performance evaluation test, considering the fact that fwseg-SNR, LLR, Cep and WSS have high correlation with overall speech quality. The correlation coefficients for these measures with speech quality are 0.84, 0.85, 0.79 and 0.64 respectively [4]. These objective measures also have good correlation with subjective scores. Although the correlation coefficient of SegSNR is 0.36, it is chosen as a time domain measure where as the above measures are of frequency domain. This paper explains the Dolph- Chebyshev Window effect on the noisy speech for Enhancement in terms of the six objective measures using Weiner Filter Speech Enhancement algorithm. The rest of the paper is organized as follows: Section-2 briefly reviews various windows for noisy Speech Enhancement. In Section-3 presents the Six Objective measure used in this study. In Section-4 Weiner Filter method for noisy speech enhancement is explained. Implementation of the scheme is explained in Section-5, Section-6 explains the results and discussions and Section- 7 Simulation Results finally Section-8 describes the conclusions. II. DATA WEIGHTING WINDOWS A. Windowing Windows are time-domain weighting functions that are used to reduce Gibbs’ oscillations resulting from the truncation of a Fourier series [5-6]. Their roots date back over one-hundred years to Fejer’s averaging technique for a truncated Fourier series and they are employed in a variety of traditional signal processing applications including power spectral estimation, beam forming, and digital filter design. The effect of a time window can be described in the frequency domain as a convolution of the frequency response of the window with the frequency