  We study the cocktail party problem and propose a novel attention network
called Tune-In, abbreviated for training under negative environments with
interference. It firstly learns two separate spaces of speaker-knowledge and
speech-stimuli based on a shared feature space, where a new block structure is
designed as the building block for all spaces, and then cooperatively solves
different tasks. Between the two spaces, information is cast towards each other
via a novel cross- and dual-attention mechanism, mimicking the bottom-up and
top-down processes of a human's cocktail party effect. It turns out that
substantially discriminative and generalizable speaker representations can be
learnt in severely interfered conditions via our self-supervised training. The
experimental results verify this seeming paradox. The learnt speaker embedding
has superior discriminative power than a standard speaker verification method;
meanwhile, Tune-In achieves remarkably better speech separation performances in
terms of SI-SNRi and SDRi consistently in all test modes, and especially at
lower memory and computational consumption, than state-of-the-art benchmark
systems.
