We present a Bayesian estimator that performs log-spectrum esti-
mation of both speech and noise, and is used as a Bayesian Kalman
filter update step for single-channel speech enhancement in the mod-
ulation 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 dis-
tribution 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 re-
sults of the phase-sensitive log-spectrum Kalman filter by comparing
them with the results obtained by traditional noise suppression tech-
niques and by an alternative Kalman filtering technique that assumes
additivity of speech and noise in the power spectral domain.