Reverberation Suppression Based on Sparse Linear Prediction in Noisy Environments

We present a single channel method for late reverberation suppression. The proposed approach estimates late reverberation as a linear combination of previous time-frequency frames. We impose a sparsity constraint on the predictor in order to select the most relevant signal frames for the estimation. The dataset used for the evaluation is corrupted by background noise, thus we propose to jointly suppress background noise and late reverberation. This leads to an important improvement in the quality of the processed signals as well as an improvement of the automatic speech recognition scores. The method appears to be efficient mainly in far field conditions and in highly reverberant environments. In addition, it is suitable for real time processing.