  Convolutional beamformers integrate the multichannel linear prediction model
into beamformers, which provide good performance and optimality for joint
dereverberation and noise reduction tasks. While longer filters are required to
model long reverberation times, the computational burden of current online
solutions grows fast with the filter length and number of microphones. In this
work, we propose a low complexity convolutional beamformer using a Kalman
filter derived affine projection algorithm to solve the adaptive filtering
problem. The proposed solution is several orders of magnitude less complex than
comparable existing solutions while slightly outperforming them on the REVERB
challenge dataset.
