Improved Gain Estimation for Codebook-Based Speech Enhancement

Codebook-based speech enhancement approaches have been proven to be able to reduce highly non-stationary noise. The key to this ability is the separation of the speech and noise spectra into gain-normalized prototype spectra-which are stored in codebooks-and gain factors, which are estimated online. Since the gain factors are estimated on a short frame basis, even rapid changes of the signal levels can be tracked accurately. There is no closed-form solution for optimal speech and noise gain factor estimation and therefore approximate solutions are required. In this paper, we use Newton's method for estimating the speech and noise gain factors. Moreover, it is shown how information from previous estimates or information from separate noise estimators can be utilized to improve estimation performance.