A third-order tensor decomposition based algorithm for speech dereverberation

Dereverberation, a crucial technique aimed at alleviating the adverse effects of reverberation, plays a central role in improving speech quality and intelligibility in speech communication and human–machine interface systems. Among many dereverberation methods, the approach utilizing delayed linear prediction is notably recognized for its effectiveness, but the resulting algorithms are in general computationally demanding. In this work, we introduce an approach that relies on the utilization of a third-order tensor decomposition to efficiently decrease computational complexity while retaining a competitive level of dereverberation performance compared to multichannel linear prediction-based algorithms. In this method, the global linear prediction filter is first represented as a composition of shorter filters through the Kronecker product (KP) decomposition. Adaptive algorithms are then derived to estimate those short filters. Complexity analysis and results from simulations showcase the advantages of the presented approach over traditional techniques.