A robust iterative inverse filtering approach for speech dereverberation in presence of disturbances

In the present work the inverse filtering problem for speech dereverberation in stationary conditions is addressed. In particular we consider the presence of multiple observables which has a beneficial impact of on room transfer functions (RTFs) invertibility. In actual acoustic environments the assumed knowledge of RTFs is usually altered by the presence of disturbances under the form of additive noise or RTF fluctuations, inevitably resulting in reduced inverse filtering performances. Several approaches, mainly based on regularization theory, have appeared in the literature to face such a problem. Among them, a recent study has shown the dereverberation capabilities dependence on some design parameters, significantly related to the filter energy. In this paper such interesting work is taken as reference and its optimum inverse filtering approach substituted with an iterative technique, which is typically much more computationally efficient. As proved by results obtained through the several computer simulations carried out, such an algorithm has revealed to be more robust w.r.t. the reference counterpart in terms of regularization parameter variations.