Codebook-Based Bayesian Speech Enhancement
Sriram Srinivasan, Johan Samuelsson, Willem Bastiaan Kleijn · 2006
In this paper, we propose a Bayesian approach for the estimation of the short-term predictor parameters of speech and noise, from the noisy observation. The resulting estimates of the speech and noise spectra can be used in a Wiener filter or any state-of-the-art speech enhancement system. We utilize a-priori information about both speech and noise in the form of trained codebooks of linear predictive coefficients. In contrast to current Bayesian estimation approaches that consider the excitation variances as part of the a-priori information, in the proposed method they are computed analytically based on the observation at hand. Consequently, the method performs well in nonstationary noise conditions. Experimental results confirm the superior performance of the proposed method compared to existing Bayesian approaches, such as those based on hidden Markov models.