Robust speech dereverberation based on non-negativity and sparse nature of speech spectrograms

This paper presents a blind dereverberation method designed to recover the subband envelope of an original speech signal from its reverberant version. The problem is formulated as a blind deconvolution problem with non-negative constraints, regularized by the sparse nature of speech spectrograms. We derive an iterative algorithm for its optimization, which can be seen as a special case of the non-negative matrix factor deconvolution. We confirmed through experiments that the algorithm is fast and robust to speaker movement.