10. Informed Spatial Filtering Based on Constrained Independent Component Analysis

In this work, we present a linearly constrained signal extraction algorithm which is based on a Minimum Mutual Information (MMI) criterion that allows to exploit the three fundamental properties of speech and audio signals: Nonstationarity, Nonwhiteness, and Nongaussianity. Hence, the proposed method is very well suited for signal processing of nonstationary nongaussian broadband signals like speech. Furthermore, from the linearly constrained MMI approach, we derive an efficient realization in a (GSC) structure. To estimate the relative transfer functions between the microphones, which are needed for the set of linear constraints, we use an informed time-domain independent component analysis algorithm, which exploits some coarse direction-of-arrival information of the target source. As a decisive advantage, this simplifies the otherwise challenging control mechanism for simultaneous adaptation of the GSC’s blocking matrix und interference and noise canceler coefficients. Finally, we establish relations between the proposed method and other well-known multichannel linear filter approaches for signal extraction based on second-order-statistics, and demonstrate the effectiveness of the proposed signal extraction method in a multispeaker scenario.