Model-based noise suppression using unsupervised estimation of hidden Markov model for non-stationary noise

Although typical model-based noise suppression including the vector Taylor series-based approach employs a single Gaussian distribution for the noise model, it is insufficient for nonstationary noises which have a complex structured distribution. As a solution to this problem, we have already proposed a method for estimating a Gaussian mixture model (GMM)-based noise model by using a minimum mean squared error (MMSE) estimate of the noise. However, the state transition process of the non-stationary noise is not modeled in the noise GMM. In this paper, we propose a way of modeling the noise with a hidden Markov model (HMM) as an extension of our previous method. The proposed method proves that the HMM-based noise model outperforms a GMM-based noise model composed of the same number of Gaussian components. In addition, we discuss the appropriate topology for the noise HMM, i.e., a leftto-right HMM and an ergodic HMM.