A tensor decomposition based multichannel linear prediction approach to speech dereverberation

Dereverberation technology is needed in a wide range of speech applications as reverberation often greatly degrades the quality and intelligibility of the speech signal of interest captured by microphones. The commonly used weighted-prediction-error method generally requires long prediction-error filters to remove the reverberation components, which makes it computationally expensive. To deal with this issue, this paper proposes a computationally efficient dereverberation algorithm based on tensor decomposition in which the long prediction-error filter is decomposed into a group of short sub-filters through multiple Kronecker products. Consequently, the high dimensional cross-correlation matrix that needs to be inverted in the dereverberation algorithm is then converted into a set of low dimensional matrices, which leads to significant reduction in the computational complexity. Simulation results demonstrate the properties of the proposed algorithm.