  The human brain can easily focus on one speaker and suppress others in
scenarios such as a cocktail party. Recently, researchers found that auditory
attention can be decoded from the electroencephalogram (EEG) data. However,
most existing deep learning methods are difficult to use prior knowledge of
different views (that is attended speech and EEG are task-related views) and
extract an unsatisfactory representation. Inspired by Broadbent's filter model,
we decode auditory attention in a multi-view paradigm and extract the most
relevant and important information utilizing the missing view. Specifically, we
propose an auditory attention decoding (AAD) method based on multi-view VAE
with task-related multi-view contrastive (TMC) learning. Employing TMC learning
in multi-view VAE can utilize the missing view to accumulate prior knowledge of
different views into the fusion of representation, and extract the approximate
task-related representation. We examine our method on two popular AAD datasets,
and demonstrate the superiority of our method by comparing it to the
state-of-the-art method.
