  The dual-path RNN (DPRNN) was proposed to more effectively model extremely
long sequences for speech separation in the time domain. By splitting long
sequences to smaller chunks and applying intra-chunk and inter-chunk RNNs, the
DPRNN reached promising performance in speech separation with a limited model
size. In this paper, we combine the DPRNN module with Convolution Recurrent
Network (CRN) and design a model called Dual-Path Convolution Recurrent Network
(DPCRN) for speech enhancement in the time-frequency domain. We replace the
RNNs in the CRN with DPRNN modules, where the intra-chunk RNNs are used to
model the spectrum pattern in a single frame and the inter-chunk RNNs are used
to model the dependence between consecutive frames. With only 0.8M parameters,
the submitted DPCRN model achieves an overall mean opinion score (MOS) of 3.57
in the wide band scenario track of the Interspeech 2021 Deep Noise Suppression
(DNS) challenge. Evaluations on some other test sets also show the efficacy of
our model.
