Autoregressive Parameter Estimation for Kalman Filtering Speech Enhancement

In this paper, autoregressive parameter estimation for Kalman filtering speech enhancement is studied. In conventional Kalman filtering speech enhancement, spectral subtraction is usually used for speech autoregressive (AR) parameter estimation. We propose log spectral amplitude (LSA) minimum mean-square error (MMSE) instead of spectral subtraction for the estimation of speech AR parameters. Based on an observation that full-band Kalman filtering speech enhancement often causes an unbalanced noise reduction between speech and non-speech segments, a spectral solution is proposed to overcome the unbalanced reduction of noise. This is done by shaping the spectral envelopes of the noise through likelihood ratio. Our simulation results show the effectiveness of the proposed method.