Low-Distortion MMSE Estimator for Speech Enhancement Based on Hahn Moments

Discrete Hahn moments are considered efficient orthogonal moments applied in various scientific areas such as signal processing and computer vision. It has a high energy compaction, considered an advantage for speech enhancement algorithm (SEA). Most conventional SEA present undesirable distortion to the improved signal. Minimizing these issues demands a robust estimator. Therefore, this paper presents Hahn moments-based linear and non-linear estimators. Wiener filter and minimum mean squared error (MMSE) sense are used to form the estimators. These estimators with Hahn moments reduce the distortion in various underlying speech conditions. The presented SEA is evaluated in terms of different quality and intelligibility measurements. The experimental results show the advantage and effectiveness of the proposed system over other existing works.