  3D speech enhancement can effectively improve the auditory experience and
plays a crucial role in augmented reality technology. However, traditional
convolutional-based speech enhancement methods have limitations in extracting
dynamic voice information. In this paper, we incorporate a dual-path recurrent
neural network block into the U-Net to iteratively extract dynamic audio
information in both the time and frequency domains. And an attention mechanism
is proposed to fuse the original signal, reference signal, and generated masks.
Moreover, we introduce a loss function to simultaneously optimize the network
in the time-frequency and time domains. Experimental results show that our
system outperforms the state-of-the-art systems on the dataset of ICASSP
L3DAS23 challenge.
