  In speech enhancement, an end-to-end deep neural network converts a noisy
speech signal to a clean speech directly in time domain without time-frequency
transformation or mask estimation. However, aggregating contextual information
from a high-resolution time domain signal with an affordable model complexity
still remains challenging. In this paper, we propose a densely connected
convolutional and recurrent network (DCCRN), a hybrid architecture, to enable
dual-staged temporal context aggregation. With the dense connectivity and
cross-component identical shortcut, DCCRN consistently outperforms competing
convolutional baselines with an average STOI improvement of 0.23 and PESQ of
1.38 at three SNR levels. The proposed method is computationally efficient with
only 1.38 million parameters. The generalizability performance on the unseen
noise types is still decent considering its low complexity, although it is
relatively weaker comparing to Wave-U-Net with 7.25 times more parameters.
