  In recent years, a number of time-domain speech separation methods have been
proposed. However, most of them are very sensitive to the environments and wide
domain coverage tasks. In this paper, from the time-frequency domain
perspective, we propose a densely-connected pyramid complex convolutional
network, termed DPCCN, to improve the robustness of speech separation under
complicated conditions. Furthermore, we generalize the DPCCN to target speech
extraction (TSE) by integrating a new specially designed speaker encoder.
Moreover, we also investigate the robustness of DPCCN to unsupervised
cross-domain TSE tasks. A Mixture-Remix approach is proposed to adapt the
target domain acoustic characteristics for fine-tuning the source model. We
evaluate the proposed methods not only under noisy and reverberant in-domain
condition, but also in clean but cross-domain conditions. Results show that for
both speech separation and extraction, the DPCCN-based systems achieve
significantly better performance and robustness than the currently dominating
time-domain methods, especially for the cross-domain tasks. Particularly, we
find that the Mixture-Remix fine-tuning with DPCCN significantly outperforms
the TD-SpeakerBeam for unsupervised cross-domain TSE, with around 3.5 dB SISNR
improvement on target domain test set, without any source domain performance
degradation.
