  Audio-visual approaches involving visual inputs have laid the foundation for
recent progress in speech separation. However, the optimization of the
concurrent usage of auditory and visual inputs is still an active research
area. Inspired by the cortico-thalamo-cortical circuit, in which the sensory
processing mechanisms of different modalities modulate one another via the
non-lemniscal sensory thalamus, we propose a novel cortico-thalamo-cortical
neural network (CTCNet) for audio-visual speech separation (AVSS). First, the
CTCNet learns hierarchical auditory and visual representations in a bottom-up
manner in separate auditory and visual subnetworks, mimicking the functions of
the auditory and visual cortical areas. Then, inspired by the large number of
connections between cortical regions and the thalamus, the model fuses the
auditory and visual information in a thalamic subnetwork through top-down
connections. Finally, the model transmits this fused information back to the
auditory and visual subnetworks, and the above process is repeated several
times. The results of experiments on three speech separation benchmark datasets
show that CTCNet remarkably outperforms existing AVSS methods with considerably
fewer parameters. These results suggest that mimicking the anatomical
connectome of the mammalian brain has great potential for advancing the
development of deep neural networks. Project repo is
https://github.com/JusperLee/CTCNet.
