Mon-1-2-9 Computationally efficient and versatile framework for joint optimization of blind speech separation and dereverberation

Abstract: This paper proposes new blind signal processing techniques foroptimizing a multi-input multi-output (MIMO) convolutionalbeamformer (CBF) in a computationally efficient way to per-form dereverberation and source separation simultaneously. Foreffective optimization of a CBF, a conventional technique fac-torizes it into a multiple-target weighted prediction error (WPE)based dereverberation filter and a separation matrix. However,this technique requires calculation of a huge matrix that repre-sents spatio-temporal covariances over different sources, whichmakes the computational cost very high. To realize computa-tionally efficient optimization, this paper introduces two tech-niques: one decomposing the huge covariance matrix into onesfor individual sources, and the other decomposing the CBF intoones for estimating individual sources. It is shown that bothtechniques effectively reduce the size of the covariance matri-ces to be calculated substantively, and allow us to greatly reducethe computational cost without loss of optimality.