Improving Robustness of Codebook-Based Noise Estimation Approaches With Delta Codebooks

Abstract

We present a new codebook-based speech enhancement approach which is able to increase robustness of conventional codebook-based approaches against model mismatch and unknown noise types. This is achieved by training only the difference between the actual noise and a robust estimate (e.g., obtained by minimum statistics or recursive minimum tracking) in the cepstral domain instead of the noise itself. The noise codebook is then generated by shifting the so obtained delta-codebook by the cepstral representation of a robust noise estimate. We use the recursive minimum tracking approach as robust estimate. It is thus guaranteed that the robust estimate is also a valid estimate of the codebook-based algorithm. Consequently, the codebook-based algorithm inherits the robustness from the recursive minimum tracking approach. Objective and subjective experiments show that the proposed method yields a consistent quality improvement over the basic codebook-based approach and recursive minimum tracking.