Cepstral weighting for speech dereverberation without musical noise

We present an effective way to reduce musical noise in binaural speech dereverberation algorithms based on an instantaneous weighting of the cepstrum. We propose this instantaneous technique, as temporal smoothing techniques result in a smearing of the signal over time and are thus expected to reduce the dereverberation performance. For the instantaneous weighting function we compute the a posteriori probability that a cepstral coefficient represents the speech spectral structure. The proposed algorithm incorporates a priori knowledge about the speech spectral structure by training the parameters of the respective likelihood function offline using a speech database. The proposed algorithm employs neither a voiced/unvoiced detection nor a fundamental period estimator and is shown to outperform an algorithm without cepstral processing in terms of a higher signal-to-interference ratio, a lower bark spectral distortion, and a lower log kurtosis ratio, indicating a reduction of musical noise.