Inter-frame modeling of DFT trajectories of speech and noise for speech enhancement using Kalman filters

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 the time-varying correlation of the temporal trajectories of the short-time (ST) DFT components of the noisy speech signal using autoregressive (AR) models. The time-varying trajectory of the DFT components of speech in each channel is modeled by a low-order AR process incorporated in the state equation of Kalman filters. The parameters of the Kalman filters are estimated recursively from the estimates of the signal and noise in DFT channels. The issue of convergence of the Kalman filters’ statistics during the noise-only periods is addressed. 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 with and without AR modeling of the DFT trajectories of noise for the enhancement of noisy speech is evaluated and compared with the MMSE log-amplitude speech estimator, parametric spectral subtraction and Wiener filter. Evaluation results show that the incorporation of spectral–temporal information through Kalman filters results in reduced residual noise and improved perceived quality of speech.