طرح مرمت دامنه و فاز آنی با استفاده از فیلتر کالمن با پیش بینی خطی کارآمد برای تقویت گفتار

This paper proposes a restoration scheme for the instantaneous amplitudes and phases in sub-bands by using a Kalman filter with linear prediction (LP). A few important studies have already proved that the phase spectrum in the short-time Fourier transform plays an important role in speech enhancement. Thus, the proposed scheme concentrates on simultaneously restoring both instantaneous amplitudes and phases. The Kalman filter, which is an optimal estimator in this scheme, is used for both instantaneous amplitudes and phases in the sub-band representation to remove the effect of noise. We found that the effectiveness of the Kalman filter depended on accurate estimates of LP coefficients. We propose an effective LP training phase to derive gender and content independent LP coefficients as central processing for Kalman filtering. We carried out objective and subjective tests under various noisy conditions to evaluate the effectiveness of the proposed scheme and compared it with typical methods. The signal to error ratio (SER), perceptual evaluation of speech quality (PESQ), and SNR loss were used as objective measures in these simulations. The mean preference score was used in subjective evaluations. The results revealed that the proposed scheme could effectively improve these objective and subjective measures more than those with typical methods.