Application of Perceptual Filtering Models to Noisy Speech Signals Enhancement
This paper describes a new speech enhancement approach using perceptually based noise reduction. The proposed approach is based on the application of two perceptual filtering models to noisy speech signals: the gamma-tone and the gamma-chirp filter banks with nonlinear resolution according to the Equivalent Rectangular Bandwidth (ERB) scale. The perceptual filtering gives a number of sub-bands that are individually spectral weighted and modified according to two different noise suppression rules. The importance of an accurate noise estimate is related to the reduction of the musical noise artifacts in the processed speech that appears after classic subtractive process. In this paper, the authors use continuous noise estimation algorithms. The performance of the proposed approach is evaluated on speech signals corrupted by real-world noises.