Speech enhancement using Kalman filters for restoration of short-time DFT trajectories

In this paper a time-frequency estimator for enhancement of noisy speech signals in the DFT domain is introduced. This estimator is based on modeling and filtering the temporal trajectories of the DFT components of noisy speech signal using Kalman filters. The time-varying trajectory of the DFT components of speech is modeled by a low order autoregressive (AR) process incorporated in the state equation of Kalman filter. A method is incorporated for restarting of Kalman filters, after long periods of noise-dominated activity in a DFT channel, to mitigate distortions of the onsets of speech activity. The performance of the proposed method for the enhancement of noisy speech is evaluated and compared with MMSE estimator and parametric spectral subtraction. Evaluation results show that the incorporation of temporal information through Kalman filters results in reduced residual noise and improved perceived quality of speech