Abstract
Speech enhancement is a very important application in many aspects. For example: a speech enhancement process can be the front-end of the speech recognition system and the digital voice communication system. It is difficult to estimate noise in the single channel case where only noisy speech can be accessed in non-stationary noise environments. A common way is to estimate noise and calculate a spectral gain to enhance speech. In this thesis, a noise estimation algorithm which modifies the noise estimation with rapid adaptation (NERA) algorithm is proposed. It combines a causal priori estimator and a speech absence probability estimator and applies the two-step noise reduction (TSNR) technique to enhance the speech signal. The experimental results show that the proposed speech enhancement algorithm can efficiently track the noise in various noise types and levels. It gets a more accurate estimation than the NERA algorithm and makes the quality of the enhanced speech better.