  In conventional multichannel audio signal enhancement, spatial and spectral
filtering are often performed sequentially. In contrast, it has been shown that
for neural spatial filtering a joint approach of spectro-spatial filtering is
more beneficial. In this contribution, we investigate the spatial filtering
performed by such a time-varying spectro-spatial filter. We extend the recently
proposed complex-valued spatial autoencoder (COSPA) for the task of target
speaker extraction by leveraging its interpretable structure and purposefully
informing the network of the target speaker's position. We show that the
resulting informed COSPA (iCOSPA) effectively and flexibly extracts a target
speaker from a mixture of speakers. We also find that the proposed architecture
is well capable of learning pronounced spatial selectivity patterns and show
that the results depend significantly on the training target and the reference
signal when computing various evaluation metrics.
