Speech enhancement with Kalman filtering the short-time DFT trajectories of noise and speech

This paper presents a time-frequency estimator for enhancement of noisy speech in the DFT domain. The time-varying trajectories of the DFT of speech and noise in each channel are modelled by low order autoregressive processes incorporated in the state equation of Kalman filters. The parameters of the Kalman filters are estimated recursively from the signal and noise in DFT channels. The issue of convergence of the Kalman filters to noise statistics during the noise-dominated periods is addressed and a method is incorporated for restarting of Kalman filters after long periods of noise-dominated activity in each DFT channel. The performance of the proposed method is compared with cases where the noise trajectories are not explicitly modelled. The sensitivity of the method to voice activity detector is evaluated. Evaluations show that the proposed method results in substantial improvement in perceived quality of speech.