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.

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

The following notice applies to all IEEE publications:
© IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

[Download]