  When using artificial neural networks for multichannel speech enhancement,
filtering is often achieved by estimating a complex-valued mask that is applied
to all or one reference channel of the input signal. The estimation of this
mask is based on the noisy multichannel signal and, hence, can exploit spatial
and spectral cues simultaneously. While it has been shown that exploiting
spatial and spectral cues jointly is beneficial for the speech enhancement
result, the mechanics of the interplay of the two inside the neural network are
still largely unknown. In this contribution, we investigate how two
conceptually different neural spatiospectral filters (NSSFs) exploit spatial
cues depending on the training target signal and show that, while one NSSF
always performs spatial filtering, the other one is selective in leveraging
spatial information depending on the task at hand. These insights provide
better understanding of the information the NSSFs use to make their prediction
and, thus, allow to make informed decisions regarding their design and
deployment.
