  To improve speech intelligibility and speech quality in noisy environments,
binaural noise reduction algorithms for head-mounted assistive listening
devices are of crucial importance. Several binaural noise reduction algorithms
such as the well-known binaural minimum variance distortionless response (MVDR)
beamformer have been proposed, which exploit spatial correlations of both the
target speech and the noise components. Furthermore, for single-microphone
scenarios, multi-frame algorithms such as the multi-frame MVDR (MFMVDR) filter
have been proposed, which exploit temporal instead of spatial correlations. In
this contribution, we propose a binaural extension of the MFMVDR filter, which
exploits both spatial and temporal correlations. The binaural MFMVDR filters
are embedded in an end-to-end deep learning framework, where the required
parameters, i.e., the speech spatio-temporal correlation vectors as well as the
(inverse) noise spatio-temporal covariance matrix, are estimated by temporal
convolutional networks (TCNs) that are trained by minimizing the mean spectral
absolute error loss function. Simulation results comprising measured binaural
room impulses and diverse noise sources at signal-to-noise ratios from -5 dB to
20 dB demonstrate the advantage of utilizing the binaural MFMVDR filter
structure over directly estimating the binaural multi-frame filter coefficients
with TCNs.
