  This paper proposes methods that can optimize a Convolutional BeamFormer
(CBF) for jointly performing denoising, dereverberation, and source separation
(DN+DR+SS) in a computationally efficient way. Conventionally, cascade
configuration composed of a Weighted Prediction Error minimization (WPE)
dereverberation filter followed by a Minimum Variance Distortionless Response
beamformer has been usedas the state-of-the-art frontend of far-field speech
recognition, however, overall optimality of this approach is not guaranteed. In
the blind signal processing area, an approach for jointly optimizing
dereverberation and source separation (DR+SS) has been proposed, however, this
approach requires huge computing cost, and has not been extended for
application to DN+DR+SS. To overcome the above limitations, this paper develops
new approaches for jointly optimizing DN+DR+SS in a computationally much more
efficient way. To this end, we first present an objective function to optimize
a CBF for performing DN+DR+SS based on the maximum likelihood estimation, on an
assumption that the steering vectors of the target signals are given or can be
estimated, e.g., using a neural network. This paper refers to a CBF optimized
by this objective function as a weighted Minimum-Power Distortionless Response
(wMPDR) CBF. Then, we derive two algorithms for optimizing a wMPDR CBF based on
two different ways of factorizing a CBF into WPE filters and beamformers.
Experiments using noisy reverberant sound mixtures show that the proposed
optimization approaches greatly improve the performance of the speech
enhancement in comparison with the conventional cascade configuration in terms
of the signal distortion measures and ASR performance. It is also shown that
the proposed approaches can greatly reduce the computing cost with improved
estimation accuracy in comparison with the conventional joint optimization
approach.
