  Recently, the convolutional weighted power minimization distortionless
response (WPD) beamformer was proposed, which unifies multi-channel weighted
prediction error dereverberation and minimum power distortionless response
beamforming. To optimize the convolutional filter, the desired speech component
is modeled with a time-varying Gaussian model, which promotes the sparsity of
the desired speech component in the short-time Fourier transform domain
compared to the noisy microphone signals. In this paper we generalize the
convolutional WPD beamformer by using an lp-norm cost function, introducing an
adjustable shape parameter which enables to control the sparsity of the desired
speech component. Experiments based on the REVERB challenge dataset show that
the proposed method outperforms the conventional convolutional WPD beamformer
in terms of objective speech quality metrics.
