Noise power estimation based on constrained sequential Gaussian mixture model for speech enhancement

An approach to estimate the noise logarithmic power was presented based on maximal likelihood. The two-component Gaussian mixture model (GMM) is utilized to describe the distribution of logarithmic power of noisy speech, where one component denotes the speech ("speech+noise") power distribution and the other component denotes the non-speech power distribution. The mean of non-speech component is optimal estimate of noise power. An on-line method is presented to update the parameter set of GMM frame by frame. Due to long-term speech absence, the on- line updation may fail. An on-line minimum description length (MDL) is presented to determine the long-term speechabsence/presence, which enables the model work well under long-term speech absence. The performance of the proposedmethod is evaluated by speech enhancement. The experimental results confirm GMM algorithm outperforms the typicalmethod such as classic MS and IMCRA algorithm.