Modulation-domain speech enhancement using a Kalman filter with a Bayesian update of speech and noise in the log-spectral domain

Abstract

We present a Bayesian estimator that performs log-spectrum estimation of both speech and noise, and is used as a Bayesian Kalman filter update step for single-channel speech enhancement in the modulation domain. We use Kalman filtering in the log-power spectral domain rather than in the amplitude or power spectral domains. In the Bayesian Kalman filter update step, we define the posterior distribution of the clean speech and noise log-power spectra as a two-dimensional multivariate Gaussian distribution. We utilize a Kalman filter observation constraint surface in the three-dimensional space, where the third dimension is the phase factor. We evaluate the results of the phase-sensitive log-spectrum Kalman filter by comparing them with the results obtained by traditional noise suppression techniques and by an alternative Kalman filtering technique that assumes additivity of speech and noise in the power spectral domain.