Speech enhancement with binaural cues derived from a priori codebook

In conventional codebook-driven speech enhancement, only spectral envelopes of speech and noise are considered, and at the same time, the type of noise is the priori information when we enhance the noisy speech. In this paper, we propose a novel codebook-based speech enhancement method which exploits a priori information about binaural cues, including clean cue and pre-enhanced cue, stored in the trained codebook. This method includes two main parts: offline training of cues and online enhancement by means of cues. That is, we use the trained codebook to model a priori information of speech and noise offline and extract the pre-enhanced cue from the noisy observation online. The clean cue is estimated by the mapping of the weighted code vectors online, and the enhanced speech is produced by the estimated clean cue. The experimental results show that the proposed approach performs better than the reference methods in both stationary and non-stationary noise condition.