Sparse target cancellation filters with application to semi-blind noise extraction

Impulse responses of filters that perform spatial null in a target direction, so-called target-cancellation filters (CFs), are usually long and dense due to the reverberant acoustic environment. It is therefore hard to blindly estimate them from noisy recordings of the target. In this paper, we show that efficient sparse CFs having many coefficients equal to zero can be designed such that their cancellation performance is tolerably lower than the performance of dense CFs. We show that an efficient sparse CF can be blindly estimated from noisy data, provided that its support is known. The resulting filter is better than a dense CF which has been blindly estimated without any prior knowledge.