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

Joint Multi-Channel Dereverberation and Noise Reduction Using a Unified Convolutional Beamformer With Sparse Priors