  Interfering sources, background noise and reverberation degrade speech
quality and intelligibility in hearing aid applications. In this paper, we
present an adaptive algorithm aiming at dereverberation, noise and interferer
reduction and preservation of binaural cues based on the wBLCMP beamformer. The
wBLCMP beamformer unifies the multi-channel weighted prediction error method
performing dereverberation and the linearly constrained minimum power
beamformer performing noise and interferer reduction into a single
convolutional beamformer. We propose to adaptively compute the optimal filter
by incorporating an exponential window into a sparsity-promoting lp-norm cost
function, which enables to track a moving target speaker. Simulation results
with successive target speakers at different positions show that the proposed
adaptive version of the wBLCMP beamformer outperforms a non-adaptive version in
terms of objective speech enhancement performance measures.
