  Studies have shown that in noisy acoustic environments, providing binaural
signals to the user of an assistive listening device may improve speech
intelligibility and spatial awareness. This paper presents a binaural speech
enhancement method using a complex convolutional neural network with an
encoder-decoder architecture and a complex multi-head attention transformer.
The model is trained to estimate individual complex ratio masks in the
time-frequency domain for the left and right-ear channels of binaural hearing
devices. The model is trained using a novel loss function that incorporates the
preservation of spatial information along with speech intelligibility
improvement and noise reduction. Simulation results for acoustic scenarios with
a single target speaker and isotropic noise of various types show that the
proposed method improves the estimated binaural speech intelligibility and
preserves the binaural cues better in comparison with several baseline
algorithms.
