  This paper describes our submission to the L3DAS22 Challenge Task 1, which
consists of speech enhancement with 3D Ambisonic microphones. The core of our
approach combines Deep Neural Network (DNN) driven complex spectral mapping
with linear beamformers such as the multi-frame multi-channel Wiener filter.
Our proposed system has two DNNs and a linear beamformer in between. Both DNNs
are trained to perform complex spectral mapping, using a combination of
waveform and magnitude spectrum losses. The estimated signal from the first DNN
is used to drive a linear beamformer, and the beamforming result, together with
this enhanced signal, are used as extra inputs for the second DNN which refines
the estimation. Then, from this new estimated signal, the linear beamformer and
second DNN are run iteratively. The proposed method was ranked first in the
challenge, achieving, on the evaluation set, a ranking metric of 0.984, versus
0.833 of the challenge baseline.
