A Discrete Fractional Cosine Transform Based Speech Enhancement System Through Adaptive Kalman Filter Combined with Perceptual Weighting Filter with Pitch Synchronous Analysis

Abstract

The speech enhancement plays a vital role commonly used in noisy environment to develop the performance of speech identification in mobile phones or in car navigation system. Thus the quality of the performance of the speech recognition is becoming worse due to the presence of noises in the surrounding. The objective is to increase the evident quality of the speech and to develop the transparency. Signal representation and enhancement in cosine transformation is observed to provide significant results. As an alternative of using DCT, a combination of conventional Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT) which forms the another transform called as the Discrete Fractional Cosine Transform (DFrCT). The DFrCT have a free parameter, its fraction. In order to deal with the issue of frame to frame deviations of the Cosine Transformations, DFrCT is integrated with Pitch Synchronous Analysis (PSA). Also Pitch Synchronous OverLap and Add (PSOLA) method are used to enhance the performance of PSA. Moreover, in order to improve the noise minimization of the system, Improved Iterative Wiener Filtering approach called Adaptive Kalman Filter Combined with Perceptual Weighting Filter is used in this approach. This filter is used to eliminate the matrix operations, reduces both the calculation time and complexity. Thus, a novel DFrCT based speech enhancement using improved iterative filtering algorithm integrated with PSA is used in this approach