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3D speech enhancement can effectively improve the auditory experience andplays a crucial role in augmented reality technology. However, traditionalconvolutional-based speech enhancement methods have limitations in extractingdynamic voice information. In this paper, we incorporate a dual-path recurrentneural network block into the U-Net to iteratively extract dynamic audioinformation in both the time and frequency domains. And an attention mechanismis proposed to fuse the original signal, reference signal, and generated masks.Moreover, we introduce a loss function to simultaneously optimize the networkin the time-frequency and time domains. Experimental results show that oursystem outperforms the state-of-the-art systems on the dataset of ICASSPL3DAS23 challenge.

Convolutional Recurrent Neural Network with Attention for 3D Speech Enhancement