  This paper proposes an approach for optimizing a Convolutional BeamFormer
(CBF) that can jointly perform denoising (DN), dereverberation (DR), and source
separation (SS). First, we develop a blind CBF optimization algorithm that
requires no prior information on the sources or the room acoustics, by
extending a conventional joint DR and SS method. For making the optimization
computationally tractable, we incorporate two techniques into the approach: the
Source-Wise Factorization (SW-Fact) of a CBF and the Independent Vector
Extraction (IVE). To further improve the performance, we develop a method that
integrates a neural network(NN) based source power spectra estimation with CBF
optimization by an inverse-Gamma prior. Experiments using noisy reverberant
mixtures reveal that our proposed method with both blind and NN-guided
scenarios greatly outperforms the conventional state-of-the-art NN-supported
mask-based CBF in terms of the improvement in automatic speech recognition and
signal distortion reduction performance.
