Generalization of Maximum A Posteriori Amplitude Estimator Under Speech Presence Uncertainty for Speech Enhancement

摘要

In this paper, we focus on the estimation-based frequency-domain speech enhancement methods under speech presence uncertainty. Through the minimization of an average risk function, a generalization of maximum a posteriori spectral amplitude estimator is derived. By adjusting the cost parameters, we can control the error caused by noise falsely detected as speech. Our experimental results show that the proposed system can be a simple alternative to Abramson's simultaneous detection and estimation approach for speech enhancement since it involves merely estimation under speech presence uncertainty and does not require any detector. Moreover, the proposed estimator takes advantage of a more straightforward implementation, since there is no need for the computation of Bessel functions.