  Signal-dependent beamformers are advantageous over signal-independent
beamformers when the acoustic scenario - be it real-world or simulated - is
straightforward in terms of the number of sound sources, the ambient sound
field and their dynamics. However, in the context of augmented reality audio
using head-worn microphone arrays, the acoustic scenarios encountered are often
far from straightforward. The design of robust, high-performance, adaptive
beamformers for such scenarios is an on-going challenge. This is due to the
violation of the typically required assumptions on the noise field caused by,
for example, rapid variations resulting from complex acoustic environments,
and/or rotations of the listener's head. This work proposes a multi-channel
speech enhancement algorithm which utilises the adaptability of
signal-dependent beamformers while still benefiting from the computational
efficiency and robust performance of signal-independent super-directive
beamformers. The algorithm has two stages. (i) The first stage is a hybrid
beamformer based on a dictionary of weights corresponding to a set of noise
field models. (ii) The second stage is a wide-band subspace post-filter to
remove any artifacts resulting from (i). The algorithm is evaluated using both
real-world recordings and simulations of a cocktail-party scenario. Noise
suppression, intelligibility and speech quality results show a significant
performance improvement by the proposed algorithm compared to the baseline
super-directive beamformer. A data-driven implementation of the noise field
dictionary is shown to provide more noise suppression, and similar speech
intelligibility and quality, compared to a parametric dictionary.
