Because the existing single channel speech enhancement technologies perform not well in the tracking and suppression of non-stationary noise

the speech enhancement method based on online energy adjustment is proposed.The normalized critical band energy parameters are employed as the feature in Gaussian mixture model (GMM) to distinguish the background noises.Based on the AR-HMM of clean speech and the noise of corresponding type

the power spectrums of speech and noise are estimated under minimum mean square error (MMSE) criteria.When the differences between the training data and test data are considered in the non-stationary noise environment

the online adjustment method for the speech and noise models is necessary.The scaling factor of speech energy is estimated with the iterative expectation maximization (EM) algorithm and the one of noise energy is estimated with the re-estimation approach similar to the training stage.And the initial scaling factor of noise energy is obtained by minima-controlled recursive averaging (MCRA) algorithm.The evaluation of the proposed method is performed under the standard of ITU-T G.160.The test results reveal that

comparing with the two reference methods

the proposed method performs well in non-stationary noise environments

including larger noise reduction and shorter convergence time.