Abstract
Audio signal enhancement often involves the application
of a time-varying filter, or suppression rule, to the
frequency-domain transform of a corrupted signal. Here we
address suppression rules derived under a Gaussian model and
interpret them as spectral estimators in a Bayesian statistical
framework. With regard to the optimal spectral amplitude
estimator of Ephraim and Malah, we show that under the same
modelling assumptions, alternative methods of Bayesian estimation
lead to much simpler suppression rules exhibiting similarly
effective behaviour. We derive three of such rules and demonstrate
that, in addition to permitting a more straightforward
implementation, they yield a more intuitive interpretation of the
Ephraim and Malah solution.