Two-Step Noise Amplitude Estimators Using Complex Gaussian Distribution Model

The estimate for the amplitude of noise signal plays an important role in many noise reduction or speech enhancement methods. However, compared with the noise power estimation, less attention has been paid to the amplitude estimation in the past years. In addition, the frequently-used voice activity detection (VAD) algorithm estimates or updates the noise amplitude during only speech absence area, which leads to an inferior performance in non-stationary noise conditions. To overcome such drawback, tow novel noise amplitude estimators working with two steps are proposed in this paper based on the assumption of complex Gaussian model. The estimation of noise power is achieved by soft decision (SD) method in the first step, and then the indirect estimators are subsequently obtained by using the relationship between the power and amplitude under the complex Gaussian distribution. The results of simulations indicated that the presented estimators can lead to significantly better speech quality than the frequently-used VAD method under various noise conditions.