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.
Highlights
We address an important issue in audio signal processing for multimedia communications, that of broadband noise reduction for audio signals via statistical modelling of their spectral components
In the first part of this paper, we have provided a common interpretation of existing suppression rules based on a simple Gaussian statistical model
While the Ephraim and Malah minimum mean square error (MMSE) spectral amplitude estimator is well known and widely used, its implementation requires the evaluation of computationally expensive exponential and Bessel functions
Summary
We employ the same model and framework to derive three new suppression rules exhibiting effective behaviour, preliminary details of which may be found in [1]. Our notation follows that of [2], except that complex quantities appear in bold
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