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Single Channel Reverberation Suppression Based on Sparse Linear Prediction

Abstract : Reverberation degrades speech intelligibility in telecommunications as well as it increases the word error rate in automatic speech recognition tasks. Several dereverberation methods have been proposed recently in order to counter these effects. In the single microphone case, the dereverberation problem is underdetermined and reverberation suppression approaches are preferred. In this paper we propose a novel method for single channel reverberation suppression. Late reverberation is estimated in the time-frequency domain as a sparse linear combination of previous frames. The predictors associated to the model are determined in a Lasso framework and a spectral subtraction filter is designed to produce the enhanced signal. This model does not require any additional information about the room acoustics and it is well suited for real-time applications. The method has state-of-the-art performance in terms of both reverberation suppression and spectral distortion.
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Nicolás López, yves Grenier, Gael Richard, Ivan Bourmeyster. Single Channel Reverberation Suppression Based on Sparse Linear Prediction. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2014, Florence, Italy. ⟨10.1109/ICASSP.2014.6854591⟩. ⟨hal-02286852⟩



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