On the eigenspace estimation for supervised multichannel system identification

Recently developed multichannel adaptive filtering algorithms aim at spatio-temporal decoupling of the signals by suitably chosen transformations. In this paper we establish the relation between the techniques of transform-domain adaptive filtering with application to multichannel acoustic cancellation. The link between the recently introduced source-domain and eigenspace adaptive filtering algorithms is shown by means of a generic spatial transform-domain adaptive filtering algorithm. We discuss the difference between regularizing the identification problem in the source domain and in the system eigenspace. Further, we study the estimation of the multiple-input multiple-output (MIMO) system eigenspace without modifying the highly cross correlated input signals or requiring prior knowledge of the system and highlight the validity of the estimated eigenspace due to system changes. Finally, we give simulation results proving our concept.