  Recording channel mismatch between training and testing conditions has been
shown to be a serious problem for speech separation. This situation greatly
reduces the separation performance, and cannot meet the requirement of daily
use. In this study, inheriting the use of our previously constructed TAT-2mix
corpus, we address the channel mismatch problem by proposing a channel-aware
audio separation network (CasNet), a deep learning framework for end-to-end
time-domain speech separation. CasNet is implemented on top of TasNet. Channel
embedding (characterizing channel information in a mixture of multiple
utterances) generated by Channel Encoder is introduced into the separation
module by the FiLM technique. Through two training strategies, we explore two
roles that channel embedding may play: 1) a real-life noise disturbance, making
the model more robust, or 2) a guide, instructing the separation model to
retain the desired channel information. Experimental results on TAT-2mix show
that CasNet trained with both training strategies outperforms the TasNet
baseline, which does not use channel embeddings.
