Enhanced minimum statistics technique incorporating soft decision for noise suppression

In this paper, we propose a novel approach to noise power estimation for robust noise suppression in noisy environments. From investigation of the state-of-the-art techniques for noise power estimation, it is discovered that the previously known methods are accurate mostly either during speech absence or speech presence but none of it works well in both situations. Our approach combines minimum statistics (MS) and soft decision (SD) techniques based on probability of speech absence. The performance of the proposed approach is evaluated by a quantitative comparison method and subjective test under various noise environments and found to yield better results compared with conventional MS and SD-based schemes.