  We present a CNN architecture for speech enhancement from multichannel
first-order Ambisonics mixtures. The data-dependent spatial filters, deduced
from a mask-based approach, are used to help an automatic speech recognition
engine to face adverse conditions of reverberation and competitive speakers.
The mask predictions are provided by a neural network, fed with rough
estimations of speech and noise amplitude spectra, under the assumption of
known directions of arrival. This study evaluates the replacing of the
recurrent LSTM network previously investigated by a convolutive U-net under
more stressing conditions with an additional second competitive speaker. We
show that, due to more accurate short-term masks prediction, the U-net
architecture brings some improvements in terms of word error rate. Moreover,
results indicate that the use of dilated convolutive layers is beneficial in
difficult situations with two interfering speakers, and/or where the target and
interferences are close to each other in terms of the angular distance.
Moreover, these results come with a two-fold reduction in the number of
parameters.
